diff --git a/.github/workflows/trufflehog.yml b/.github/workflows/trufflehog.yml
new file mode 100644
index 00000000..9cbbf680
--- /dev/null
+++ b/.github/workflows/trufflehog.yml
@@ -0,0 +1,15 @@
+on:
+  push:
+
+name: Secret Leaks
+
+jobs:
+  trufflehog:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+    - name: Secret Scanning
+      uses: trufflesecurity/trufflehog@main
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
new file mode 100644
index 00000000..b23f3150
--- /dev/null
+++ b/CODE_OF_CONDUCT.md
@@ -0,0 +1,133 @@
+
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, caste, color, religion, or sexual
+identity and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the overall
+  community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or advances of
+  any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email address,
+  without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+feedback@huggingface.co.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series of
+actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or permanent
+ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior, harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within the
+community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.1, available at
+[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
+
+Community Impact Guidelines were inspired by
+[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
+
+For answers to common questions about this code of conduct, see the FAQ at
+[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
+[https://www.contributor-covenant.org/translations][translations].
+
+[homepage]: https://www.contributor-covenant.org
+[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
+[Mozilla CoC]: https://github.com/mozilla/diversity
+[FAQ]: https://www.contributor-covenant.org/faq
+[translations]: https://www.contributor-covenant.org/translations
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 00000000..57901164
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,111 @@
+<!---
+Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Contribute to text-embeddings-inference
+
+Everyone is welcome to contribute, and we value everybody's contribution. Code
+contributions are not the only way to help the community. Answering questions, helping
+others, and improving the documentation are also immensely valuable.
+
+It also helps us if you spread the word! Reference the library in blog posts
+about the awesome projects it made possible, shout out on Twitter every time it has
+helped you, or simply ⭐️ the repository to say thank you.
+
+However you choose to contribute, please be mindful and respect our
+[code of conduct](https://github.com/huggingface/text-embeddings-inference/blob/main/CODE_OF_CONDUCT.md).
+
+**This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md).**
+
+## Ways to contribute
+
+There are several ways you can contribute to text-embeddings-inference.
+
+* Fix outstanding issues with the existing code.
+* Submit issues related to bugs or desired new features.
+* Contribute to the examples or to the documentation.
+
+> All contributions are equally valuable to the community. 🥰
+
+## Fixing outstanding issues
+
+If you notice an issue with the existing code and have a fix in mind, feel free to [start contributing](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request) and open
+a Pull Request!
+
+## Submitting a bug-related issue or feature request
+
+Do your best to follow these guidelines when submitting a bug-related issue or a feature
+request. It will make it easier for us to come back to you quickly and with good
+feedback.
+
+### Did you find a bug?
+
+The text-embeddings-inference library is robust and reliable thanks to users who report the problems they encounter.
+
+Before you report an issue, we would really appreciate it if you could **make sure the bug was not
+already reported** (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the
+library itself, and not your code.
+
+Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so
+we can quickly resolve it:
+
+* Your **OS type and version**, as well as your environment versions (versions of rust, python, and dependencies).
+* A short, self-contained, code snippet that allows us to reproduce the bug.
+* The *full* traceback if an exception is raised.
+* Attach any other additional information, like screenshots, you think may help.
+
+### Do you want a new feature?
+
+If there is a new feature you'd like to see in text-embeddings-inference, please open an issue and describe:
+
+1. What is the *motivation* behind this feature? Is it related to a problem or frustration with the library? Is it
+   a feature related to something you need for a project? Is it something you worked on and think it could benefit
+   the community?
+
+   Whatever it is, we'd love to hear about it!
+
+2. Describe your requested feature in as much detail as possible. The more you can tell us about it, the better
+   we'll be able to help you.
+3. Provide a *code snippet* that demonstrates the feature's usage.
+4. If the feature is related to a paper, please include a link.
+
+If your issue is well written we're already 80% of the way there by the time you create it.
+
+We have added [templates](https://github.com/huggingface/text-embeddings-inference/tree/main/.github/ISSUE_TEMPLATE)
+to help you get started with your issue.
+
+## Do you want to implement a new model?
+
+New models are constantly released and if you want to implement a new model, please provide the following information:
+
+* A short description of the model and a link to the paper.
+* Link to the implementation if it is open-sourced.
+* Link to the model weights if they are available.
+
+If you are willing to contribute the model yourself, let us know so we can help you add it to text-embeddings-inference!
+
+## Do you want to add documentation?
+
+We're always looking for improvements to the documentation that make it more clear and accurate. Please let us know
+how the documentation can be improved such as typos and any content that is missing, unclear or inaccurate. We'll be
+happy to make the changes or help you make a contribution if you're interested!
+
+## I want to become a maintainer of the project. How do I get there?
+
+TGI is a project led and managed by Hugging Face as it powers our internal services. However, we are happy to have
+motivated individuals from other organizations join us as maintainers with the goal of making TGI the best inference
+service.
+
+If you are such an individual (or organization), please reach out to us and let's collaborate.
diff --git a/Cargo.lock b/Cargo.lock
index dba09726..e29254ad 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -10,9 +10,9 @@ checksum = "415ed64958754dbe991900f3940677e6a7eefb4d7367afd70d642677b0c7d19d"
 
 [[package]]
 name = "addr2line"
-version = "0.21.0"
+version = "0.22.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8a30b2e23b9e17a9f90641c7ab1549cd9b44f296d3ccbf309d2863cfe398a0cb"
+checksum = "6e4503c46a5c0c7844e948c9a4d6acd9f50cccb4de1c48eb9e291ea17470c678"
 dependencies = [
  "gimli",
 ]
@@ -61,47 +61,48 @@ dependencies = [
 
 [[package]]
 name = "anstream"
-version = "0.6.13"
+version = "0.6.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d96bd03f33fe50a863e394ee9718a706f988b9079b20c3784fb726e7678b62fb"
+checksum = "418c75fa768af9c03be99d17643f93f79bbba589895012a80e3452a19ddda15b"
 dependencies = [
  "anstyle",
  "anstyle-parse",
  "anstyle-query",
  "anstyle-wincon",
  "colorchoice",
+ "is_terminal_polyfill",
  "utf8parse",
 ]
 
 [[package]]
 name = "anstyle"
-version = "1.0.6"
+version = "1.0.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8901269c6307e8d93993578286ac0edf7f195079ffff5ebdeea6a59ffb7e36bc"
+checksum = "038dfcf04a5feb68e9c60b21c9625a54c2c0616e79b72b0fd87075a056ae1d1b"
 
 [[package]]
 name = "anstyle-parse"
-version = "0.2.3"
+version = "0.2.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c75ac65da39e5fe5ab759307499ddad880d724eed2f6ce5b5e8a26f4f387928c"
+checksum = "c03a11a9034d92058ceb6ee011ce58af4a9bf61491aa7e1e59ecd24bd40d22d4"
 dependencies = [
  "utf8parse",
 ]
 
 [[package]]
 name = "anstyle-query"
-version = "1.0.2"
+version = "1.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e28923312444cdd728e4738b3f9c9cac739500909bb3d3c94b43551b16517648"
+checksum = "ad186efb764318d35165f1758e7dcef3b10628e26d41a44bc5550652e6804391"
 dependencies = [
  "windows-sys 0.52.0",
 ]
 
 [[package]]
 name = "anstyle-wincon"
-version = "3.0.2"
+version = "3.0.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1cd54b81ec8d6180e24654d0b371ad22fc3dd083b6ff8ba325b72e00c87660a7"
+checksum = "61a38449feb7068f52bb06c12759005cf459ee52bb4adc1d5a7c4322d716fb19"
 dependencies = [
  "anstyle",
  "windows-sys 0.52.0",
@@ -109,13 +110,34 @@ dependencies = [
 
 [[package]]
 name = "anyhow"
-version = "1.0.82"
+version = "1.0.86"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f538837af36e6f6a9be0faa67f9a314f8119e4e4b5867c6ab40ed60360142519"
+checksum = "b3d1d046238990b9cf5bcde22a3fb3584ee5cf65fb2765f454ed428c7a0063da"
 dependencies = [
  "backtrace",
 ]
 
+[[package]]
+name = "arbitrary"
+version = "1.3.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7d5a26814d8dcb93b0e5a0ff3c6d80a8843bafb21b39e8e18a6f05471870e110"
+dependencies = [
+ "derive_arbitrary",
+]
+
+[[package]]
+name = "async-channel"
+version = "2.3.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "89b47800b0be77592da0afd425cc03468052844aff33b84e33cc696f64e77b6a"
+dependencies = [
+ "concurrent-queue",
+ "event-listener-strategy",
+ "futures-core",
+ "pin-project-lite",
+]
+
 [[package]]
 name = "async-stream"
 version = "0.3.5"
@@ -135,7 +157,7 @@ checksum = "16e62a023e7c117e27523144c5d2459f4397fcc3cab0085af8e2224f643a0193"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -146,14 +168,47 @@ checksum = "c6fa2087f2753a7da8cc1c0dbfcf89579dd57458e36769de5ac750b4671737ca"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
+[[package]]
+name = "atomic-waker"
+version = "1.1.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1505bd5d3d116872e7271a6d4e16d81d0c8570876c8de68093a09ac269d8aac0"
+
 [[package]]
 name = "autocfg"
-version = "1.2.0"
+version = "1.3.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0c4b4d0bd25bd0b74681c0ad21497610ce1b7c91b1022cd21c80c6fbdd9476b0"
+
+[[package]]
+name = "aws-lc-rs"
+version = "1.8.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a8a47f2fb521b70c11ce7369a6c5fa4bd6af7e5d62ec06303875bafe7c6ba245"
+dependencies = [
+ "aws-lc-sys",
+ "mirai-annotations",
+ "paste",
+ "zeroize",
+]
+
+[[package]]
+name = "aws-lc-sys"
+version = "0.19.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f1fdabc7756949593fe60f30ec81974b613357de856987752631dea1e3394c80"
+checksum = "2927c7af777b460b7ccd95f8b67acd7b4c04ec8896bf0c8e80ba30523cffc057"
+dependencies = [
+ "bindgen",
+ "cc",
+ "cmake",
+ "dunce",
+ "fs_extra",
+ "libc",
+ "paste",
+]
 
 [[package]]
 name = "axum"
@@ -168,7 +223,7 @@ dependencies = [
  "futures-util",
  "http 0.2.12",
  "http-body 0.4.6",
- "hyper 0.14.28",
+ "hyper 0.14.29",
  "itoa",
  "matchit",
  "memchr",
@@ -196,7 +251,7 @@ dependencies = [
  "http 1.1.0",
  "http-body 1.0.0",
  "http-body-util",
- "hyper 1.3.1",
+ "hyper 1.4.0",
  "hyper-util",
  "itoa",
  "matchit",
@@ -257,25 +312,25 @@ dependencies = [
 
 [[package]]
 name = "axum-tracing-opentelemetry"
-version = "0.17.1"
+version = "0.18.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "91387a3e9f6aa45f112cd05d1e5430f9ba40b51440849e4760a5dd51b736149f"
+checksum = "f26965dca35be1ca4a128177b9302c4c04f0661443621171adb09d002dcdf1d5"
 dependencies = [
  "axum 0.7.5",
  "futures-core",
  "futures-util",
  "http 1.1.0",
- "opentelemetry 0.21.0",
+ "opentelemetry 0.22.0",
  "pin-project-lite",
  "tower",
  "tracing",
- "tracing-opentelemetry 0.22.0",
+ "tracing-opentelemetry 0.23.0",
  "tracing-opentelemetry-instrumentation-sdk",
 ]
 
 [[package]]
 name = "backend-grpc-client"
-version = "1.2.2"
+version = "1.3.0"
 dependencies = [
  "grpc-metadata",
  "prost 0.11.9",
@@ -290,9 +345,9 @@ dependencies = [
 
 [[package]]
 name = "backtrace"
-version = "0.3.71"
+version = "0.3.73"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "26b05800d2e817c8b3b4b54abd461726265fa9789ae34330622f2db9ee696f9d"
+checksum = "5cc23269a4f8976d0a4d2e7109211a419fe30e8d88d677cd60b6bc79c5732e0a"
 dependencies = [
  "addr2line",
  "cc",
@@ -323,9 +378,32 @@ checksum = "9d297deb1925b89f2ccc13d7635fa0714f12c87adce1c75356b39ca9b7178567"
 
 [[package]]
 name = "base64"
-version = "0.22.0"
+version = "0.22.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9475866fec1451be56a3c2400fd081ff546538961565ccb5b7142cbd22bc7a51"
+checksum = "72b3254f16251a8381aa12e40e3c4d2f0199f8c6508fbecb9d91f575e0fbb8c6"
+
+[[package]]
+name = "bindgen"
+version = "0.69.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a00dc851838a2120612785d195287475a3ac45514741da670b735818822129a0"
+dependencies = [
+ "bitflags 2.6.0",
+ "cexpr",
+ "clang-sys",
+ "itertools 0.12.1",
+ "lazy_static",
+ "lazycell",
+ "log",
+ "prettyplease 0.2.20",
+ "proc-macro2",
+ "quote",
+ "regex",
+ "rustc-hash",
+ "shlex",
+ "syn 2.0.68",
+ "which",
+]
 
 [[package]]
 name = "bindgen_cuda"
@@ -361,9 +439,9 @@ checksum = "bef38d45163c2f1dde094a7dfd33ccf595c92905c8f8f4fdc18d06fb1037718a"
 
 [[package]]
 name = "bitflags"
-version = "2.5.0"
+version = "2.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "cf4b9d6a944f767f8e5e0db018570623c85f3d925ac718db4e06d0187adb21c1"
+checksum = "b048fb63fd8b5923fc5aa7b340d8e156aec7ec02f0c78fa8a6ddc2613f6f71de"
 
 [[package]]
 name = "block"
@@ -388,22 +466,22 @@ checksum = "79296716171880943b8470b5f8d03aa55eb2e645a4874bdbb28adb49162e012c"
 
 [[package]]
 name = "bytemuck"
-version = "1.15.0"
+version = "1.16.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5d6d68c57235a3a081186990eca2867354726650f42f7516ca50c28d6281fd15"
+checksum = "b236fc92302c97ed75b38da1f4917b5cdda4984745740f153a5d3059e48d725e"
 dependencies = [
  "bytemuck_derive",
 ]
 
 [[package]]
 name = "bytemuck_derive"
-version = "1.6.0"
+version = "1.7.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4da9a32f3fed317401fa3c862968128267c3106685286e15d5aaa3d7389c2f60"
+checksum = "1ee891b04274a59bd38b412188e24b849617b2e45a0fd8d057deb63e7403761b"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -442,7 +520,7 @@ dependencies = [
  "safetensors",
  "thiserror",
  "yoke",
- "zip",
+ "zip 0.6.6",
 ]
 
 [[package]]
@@ -558,9 +636,23 @@ dependencies = [
 
 [[package]]
 name = "cc"
-version = "1.0.94"
+version = "1.0.104"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "74b6a57f98764a267ff415d50a25e6e166f3831a5071af4995296ea97d210490"
+dependencies = [
+ "jobserver",
+ "libc",
+ "once_cell",
+]
+
+[[package]]
+name = "cexpr"
+version = "0.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "17f6e324229dc011159fcc089755d1e2e216a90d43a7dea6853ca740b84f35e7"
+checksum = "6fac387a98bb7c37292057cffc56d62ecb629900026402633ae9160df93a8766"
+dependencies = [
+ "nom",
+]
 
 [[package]]
 name = "cfg-if"
@@ -582,11 +674,22 @@ dependencies = [
  "windows-targets 0.52.5",
 ]
 
+[[package]]
+name = "clang-sys"
+version = "1.8.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0b023947811758c97c59bf9d1c188fd619ad4718dcaa767947df1cadb14f39f4"
+dependencies = [
+ "glob",
+ "libc",
+ "libloading",
+]
+
 [[package]]
 name = "clap"
-version = "4.5.4"
+version = "4.5.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "90bc066a67923782aa8515dbaea16946c5bcc5addbd668bb80af688e53e548a0"
+checksum = "84b3edb18336f4df585bc9aa31dd99c036dfa5dc5e9a2939a722a188f3a8970d"
 dependencies = [
  "clap_builder",
  "clap_derive",
@@ -594,39 +697,57 @@ dependencies = [
 
 [[package]]
 name = "clap_builder"
-version = "4.5.2"
+version = "4.5.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ae129e2e766ae0ec03484e609954119f123cc1fe650337e155d03b022f24f7b4"
+checksum = "c1c09dd5ada6c6c78075d6fd0da3f90d8080651e2d6cc8eb2f1aaa4034ced708"
 dependencies = [
  "anstream",
  "anstyle",
  "clap_lex",
- "strsim 0.11.1",
+ "strsim",
 ]
 
 [[package]]
 name = "clap_derive"
-version = "4.5.4"
+version = "4.5.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "528131438037fd55894f62d6e9f068b8f45ac57ffa77517819645d10aed04f64"
+checksum = "2bac35c6dafb060fd4d275d9a4ffae97917c13a6327903a8be2153cd964f7085"
 dependencies = [
  "heck 0.5.0",
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
 name = "clap_lex"
-version = "0.7.0"
+version = "0.7.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4b82cf0babdbd58558212896d1a4272303a57bdb245c2bf1147185fb45640e70"
+
+[[package]]
+name = "cmake"
+version = "0.1.50"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "98cc8fbded0c607b7ba9dd60cd98df59af97e84d24e49c8557331cfc26d301ce"
+checksum = "a31c789563b815f77f4250caee12365734369f942439b7defd71e18a48197130"
+dependencies = [
+ "cc",
+]
 
 [[package]]
 name = "colorchoice"
-version = "1.0.0"
+version = "1.0.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0b6a852b24ab71dffc585bcb46eaf7959d175cb865a7152e35b348d1b2960422"
+
+[[package]]
+name = "concurrent-queue"
+version = "2.5.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "acbf1af155f9b9ef647e42cdc158db4b64a1b61f743629225fde6f3e0be2a7c7"
+checksum = "4ca0197aee26d1ae37445ee532fefce43251d24cc7c166799f4d46817f1d3973"
+dependencies = [
+ "crossbeam-utils",
+]
 
 [[package]]
 name = "console"
@@ -679,18 +800,18 @@ dependencies = [
 
 [[package]]
 name = "crc32fast"
-version = "1.4.0"
+version = "1.4.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b3855a8a784b474f333699ef2bbca9db2c4a1f6d9088a90a2d25b1eb53111eaa"
+checksum = "a97769d94ddab943e4510d138150169a2758b5ef3eb191a9ee688de3e23ef7b3"
 dependencies = [
  "cfg-if",
 ]
 
 [[package]]
 name = "crossbeam-channel"
-version = "0.5.12"
+version = "0.5.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ab3db02a9c5b5121e1e42fbdb1aeb65f5e02624cc58c43f2884c6ccac0b82f95"
+checksum = "33480d6946193aa8033910124896ca395333cae7e2d1113d1fef6c3272217df2"
 dependencies = [
  "crossbeam-utils",
 ]
@@ -716,9 +837,9 @@ dependencies = [
 
 [[package]]
 name = "crossbeam-utils"
-version = "0.8.19"
+version = "0.8.20"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "248e3bacc7dc6baa3b21e405ee045c3047101a49145e7e9eca583ab4c2ca5345"
+checksum = "22ec99545bb0ed0ea7bb9b8e1e9122ea386ff8a48c0922e43f36d45ab09e0e80"
 
 [[package]]
 name = "crunchy"
@@ -746,72 +867,37 @@ dependencies = [
 
 [[package]]
 name = "darling"
-version = "0.14.4"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7b750cb3417fd1b327431a470f388520309479ab0bf5e323505daf0290cd3850"
-dependencies = [
- "darling_core 0.14.4",
- "darling_macro 0.14.4",
-]
-
-[[package]]
-name = "darling"
-version = "0.20.8"
+version = "0.20.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "54e36fcd13ed84ffdfda6f5be89b31287cbb80c439841fe69e04841435464391"
+checksum = "83b2eb4d90d12bdda5ed17de686c2acb4c57914f8f921b8da7e112b5a36f3fe1"
 dependencies = [
- "darling_core 0.20.8",
- "darling_macro 0.20.8",
+ "darling_core",
+ "darling_macro",
 ]
 
 [[package]]
 name = "darling_core"
-version = "0.14.4"
+version = "0.20.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "109c1ca6e6b7f82cc233a97004ea8ed7ca123a9af07a8230878fcfda9b158bf0"
+checksum = "622687fe0bac72a04e5599029151f5796111b90f1baaa9b544d807a5e31cd120"
 dependencies = [
  "fnv",
  "ident_case",
  "proc-macro2",
  "quote",
- "strsim 0.10.0",
- "syn 1.0.109",
-]
-
-[[package]]
-name = "darling_core"
-version = "0.20.8"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9c2cf1c23a687a1feeb728783b993c4e1ad83d99f351801977dd809b48d0a70f"
-dependencies = [
- "fnv",
- "ident_case",
- "proc-macro2",
- "quote",
- "strsim 0.10.0",
- "syn 2.0.59",
-]
-
-[[package]]
-name = "darling_macro"
-version = "0.14.4"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a4aab4dbc9f7611d8b55048a3a16d2d010c2c8334e46304b40ac1cc14bf3b48e"
-dependencies = [
- "darling_core 0.14.4",
- "quote",
- "syn 1.0.109",
+ "strsim",
+ "syn 2.0.68",
 ]
 
 [[package]]
 name = "darling_macro"
-version = "0.20.8"
+version = "0.20.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a668eda54683121533a393014d8692171709ff57a7d61f187b6e782719f8933f"
+checksum = "733cabb43482b1a1b53eee8583c2b9e8684d592215ea83efd305dd31bc2f0178"
 dependencies = [
- "darling_core 0.20.8",
+ "darling_core",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -821,7 +907,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "978747c1d849a7d2ee5e8adc0159961c48fb7e5db2f06af6723b80123bb53856"
 dependencies = [
  "cfg-if",
- "hashbrown 0.14.3",
+ "hashbrown 0.14.5",
  "lock_api",
  "once_cell",
  "parking_lot_core",
@@ -837,12 +923,14 @@ dependencies = [
 ]
 
 [[package]]
-name = "derive_builder"
-version = "0.12.0"
+name = "derive_arbitrary"
+version = "1.3.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8d67778784b508018359cbc8696edb3db78160bab2c2a28ba7f56ef6932997f8"
+checksum = "67e77553c4162a157adbf834ebae5b415acbecbeafc7a74b0e886657506a7611"
 dependencies = [
- "derive_builder_macro 0.12.0",
+ "proc-macro2",
+ "quote",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -851,19 +939,7 @@ version = "0.20.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "0350b5cb0331628a5916d6c5c0b72e97393b8b6b03b47a9284f4e7f5a405ffd7"
 dependencies = [
- "derive_builder_macro 0.20.0",
-]
-
-[[package]]
-name = "derive_builder_core"
-version = "0.12.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c11bdc11a0c47bc7d37d582b5285da6849c96681023680b906673c5707af7b0f"
-dependencies = [
- "darling 0.14.4",
- "proc-macro2",
- "quote",
- "syn 1.0.109",
+ "derive_builder_macro",
 ]
 
 [[package]]
@@ -872,20 +948,10 @@ version = "0.20.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "d48cda787f839151732d396ac69e3473923d54312c070ee21e9effcaa8ca0b1d"
 dependencies = [
- "darling 0.20.8",
+ "darling",
  "proc-macro2",
  "quote",
- "syn 2.0.59",
-]
-
-[[package]]
-name = "derive_builder_macro"
-version = "0.12.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ebcda35c7a396850a55ffeac740804b40ffec779b98fffbb1738f4033f0ee79e"
-dependencies = [
- "derive_builder_core 0.12.0",
- "syn 1.0.109",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -894,8 +960,8 @@ version = "0.20.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "206868b8242f27cecce124c19fd88157fbd0dd334df2587f36417bafbc85097b"
 dependencies = [
- "derive_builder_core 0.20.0",
- "syn 2.0.59",
+ "derive_builder_core",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -938,6 +1004,23 @@ dependencies = [
  "windows-sys 0.48.0",
 ]
 
+[[package]]
+name = "displaydoc"
+version = "0.2.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "97369cbbc041bc366949bc74d34658d6cda5621039731c6310521892a3a20ae0"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.68",
+]
+
+[[package]]
+name = "dunce"
+version = "1.0.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "56ce8c6da7551ec6c462cbaf3bfbc75131ebbfa1c944aeaa9dab51ca1c5f0c3b"
+
 [[package]]
 name = "dyn-stack"
 version = "0.10.0"
@@ -950,9 +1033,9 @@ dependencies = [
 
 [[package]]
 name = "either"
-version = "1.11.0"
+version = "1.13.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a47c1c47d2f5964e29c61246e81db715514cd532db6b5116a25ea3c03d6780a2"
+checksum = "60b1af1c220855b6ceac025d3f6ecdd2b7c4894bfe9cd9bda4fbb4bc7c0d4cf0"
 
 [[package]]
 name = "encode_unicode"
@@ -978,7 +1061,7 @@ dependencies = [
  "heck 0.4.1",
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -989,9 +1072,9 @@ checksum = "5443807d6dff69373d433ab9ef5378ad8df50ca6298caf15de6e52e24aaf54d5"
 
 [[package]]
 name = "errno"
-version = "0.3.8"
+version = "0.3.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a258e46cdc063eb8519c00b9fc845fc47bcfca4130e2f08e88665ceda8474245"
+checksum = "534c5cf6194dfab3db3242765c03bbe257cf92f22b38f6bc0c58d59108a820ba"
 dependencies = [
  "libc",
  "windows-sys 0.52.0",
@@ -1006,6 +1089,27 @@ dependencies = [
  "cc",
 ]
 
+[[package]]
+name = "event-listener"
+version = "5.3.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6032be9bd27023a771701cc49f9f053c751055f71efb2e0ae5c15809093675ba"
+dependencies = [
+ "concurrent-queue",
+ "parking",
+ "pin-project-lite",
+]
+
+[[package]]
+name = "event-listener-strategy"
+version = "0.5.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0f214dc438f977e6d4e3500aaa277f5ad94ca83fbbd9b1a15713ce2344ccc5a1"
+dependencies = [
+ "event-listener",
+ "pin-project-lite",
+]
+
 [[package]]
 name = "fancy-regex"
 version = "0.13.0"
@@ -1013,15 +1117,15 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "531e46835a22af56d1e3b66f04844bed63158bc094a628bec1d321d9b4c44bf2"
 dependencies = [
  "bit-set",
- "regex-automata 0.4.6",
- "regex-syntax 0.8.3",
+ "regex-automata 0.4.7",
+ "regex-syntax 0.8.4",
 ]
 
 [[package]]
 name = "fastrand"
-version = "2.0.2"
+version = "2.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "658bd65b1cf4c852a3cc96f18a8ce7b5640f6b703f905c7d74532294c2a63984"
+checksum = "9fc0510504f03c51ada170672ac806f1f105a88aa97a5281117e1ddc3368e51a"
 
 [[package]]
 name = "filetime"
@@ -1031,7 +1135,7 @@ checksum = "1ee447700ac8aa0b2f2bd7bc4462ad686ba06baa6727ac149a2d6277f0d240fd"
 dependencies = [
  "cfg-if",
  "libc",
- "redox_syscall",
+ "redox_syscall 0.4.1",
  "windows-sys 0.52.0",
 ]
 
@@ -1043,9 +1147,9 @@ checksum = "0ce7134b9999ecaf8bcd65542e436736ef32ddca1b3e06094cb6ec5755203b80"
 
 [[package]]
 name = "flate2"
-version = "1.0.28"
+version = "1.0.30"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "46303f565772937ffe1d394a4fac6f411c6013172fadde9dcdb1e147a086940e"
+checksum = "5f54427cfd1c7829e2a139fcefea601bf088ebca651d2bf53ebc600eac295dae"
 dependencies = [
  "crc32fast",
  "miniz_oxide",
@@ -1084,7 +1188,7 @@ checksum = "1a5c6c585bc94aaf2c7b51dd4c2ba22680844aba4c687be581871a6f518c5742"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -1108,6 +1212,12 @@ dependencies = [
  "percent-encoding",
 ]
 
+[[package]]
+name = "fs_extra"
+version = "1.3.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "42703706b716c37f96a77aea830392ad231f44c9e9a67872fa5548707e11b11c"
+
 [[package]]
 name = "futures"
 version = "0.3.30"
@@ -1164,7 +1274,7 @@ checksum = "87750cf4b7a4c0625b1529e4c543c2182106e4dedc60a2a6455e00d212c489ac"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -1213,7 +1323,7 @@ dependencies = [
  "num-complex",
  "num-traits",
  "paste",
- "raw-cpuid",
+ "raw-cpuid 10.7.0",
  "seq-macro",
 ]
 
@@ -1228,7 +1338,7 @@ dependencies = [
  "num-complex",
  "num-traits",
  "paste",
- "raw-cpuid",
+ "raw-cpuid 10.7.0",
  "seq-macro",
 ]
 
@@ -1243,7 +1353,7 @@ dependencies = [
  "num-complex",
  "num-traits",
  "paste",
- "raw-cpuid",
+ "raw-cpuid 10.7.0",
  "seq-macro",
 ]
 
@@ -1261,7 +1371,7 @@ dependencies = [
  "once_cell",
  "paste",
  "pulp",
- "raw-cpuid",
+ "raw-cpuid 10.7.0",
  "rayon",
  "seq-macro",
  "sysctl",
@@ -1280,7 +1390,7 @@ dependencies = [
  "num-complex",
  "num-traits",
  "paste",
- "raw-cpuid",
+ "raw-cpuid 10.7.0",
  "rayon",
  "seq-macro",
 ]
@@ -1296,7 +1406,7 @@ dependencies = [
  "num-complex",
  "num-traits",
  "paste",
- "raw-cpuid",
+ "raw-cpuid 10.7.0",
  "seq-macro",
 ]
 
@@ -1311,7 +1421,7 @@ dependencies = [
  "num-complex",
  "num-traits",
  "paste",
- "raw-cpuid",
+ "raw-cpuid 10.7.0",
  "seq-macro",
 ]
 
@@ -1327,9 +1437,9 @@ dependencies = [
 
 [[package]]
 name = "getrandom"
-version = "0.2.14"
+version = "0.2.15"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "94b22e06ecb0110981051723910cbf0b5f5e09a2062dd7663334ee79a9d1286c"
+checksum = "c4567c8db10ae91089c99af84c68c38da3ec2f087c3f82960bcdbf3656b6f4d7"
 dependencies = [
  "cfg-if",
  "libc",
@@ -1350,9 +1460,9 @@ dependencies = [
 
 [[package]]
 name = "gimli"
-version = "0.28.1"
+version = "0.29.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4271d37baee1b8c7e4b708028c57d816cf9d2434acb33a549475f78c181f6253"
+checksum = "40ecd4077b5ae9fd2e9e169b102c6c330d0605168eb0e8bf79952b256dbefffd"
 
 [[package]]
 name = "glob"
@@ -1389,6 +1499,25 @@ dependencies = [
  "tracing",
 ]
 
+[[package]]
+name = "h2"
+version = "0.4.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "fa82e28a107a8cc405f0839610bdc9b15f1e25ec7d696aa5cf173edbcb1486ab"
+dependencies = [
+ "atomic-waker",
+ "bytes",
+ "fnv",
+ "futures-core",
+ "futures-sink",
+ "http 1.1.0",
+ "indexmap 2.2.6",
+ "slab",
+ "tokio",
+ "tokio-util",
+ "tracing",
+]
+
 [[package]]
 name = "half"
 version = "2.4.1"
@@ -1411,19 +1540,13 @@ checksum = "8a9ee70c43aaf417c914396645a0fa852624801b24ebb7ae78fe8272889ac888"
 
 [[package]]
 name = "hashbrown"
-version = "0.13.1"
+version = "0.14.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "33ff8ae62cd3a9102e5637afc8452c55acf3844001bd5374e0b0bd7b6616c038"
+checksum = "e5274423e17b7c9fc20b6e7e208532f9b19825d82dfd615708b70edd83df41f1"
 dependencies = [
  "ahash",
 ]
 
-[[package]]
-name = "hashbrown"
-version = "0.14.3"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "290f1a1d9242c78d09ce40a5e87e7554ee637af1351968159f4952f028f75604"
-
 [[package]]
 name = "heck"
 version = "0.4.1"
@@ -1445,17 +1568,17 @@ checksum = "d231dfb89cfffdbc30e7fc41579ed6066ad03abda9e567ccafae602b97ec5024"
 [[package]]
 name = "hf-hub"
 version = "0.3.2"
-source = "git+https://github.com/huggingface/hf-hub?rev=b167f69692be5f49eb8003788f7f8a499a98b096#b167f69692be5f49eb8003788f7f8a499a98b096"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2b780635574b3d92f036890d8373433d6f9fc7abb320ee42a5c25897fc8ed732"
 dependencies = [
  "dirs",
  "futures",
- "http 1.1.0",
  "indicatif",
  "log",
  "native-tls",
  "num_cpus",
  "rand",
- "reqwest",
+ "reqwest 0.11.27",
  "serde",
  "serde_json",
  "thiserror",
@@ -1517,12 +1640,12 @@ dependencies = [
 
 [[package]]
 name = "http-body-util"
-version = "0.1.1"
+version = "0.1.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0475f8b2ac86659c21b64320d5d653f9efe42acd2a4e560073ec61a155a34f1d"
+checksum = "793429d76616a256bcb62c2a2ec2bed781c8307e797e2598c50010f2bee2544f"
 dependencies = [
  "bytes",
- "futures-core",
+ "futures-util",
  "http 1.1.0",
  "http-body 1.0.0",
  "pin-project-lite",
@@ -1530,9 +1653,9 @@ dependencies = [
 
 [[package]]
 name = "httparse"
-version = "1.8.0"
+version = "1.9.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d897f394bad6a705d5f4104762e116a75639e470d80901eed05a860a95cb1904"
+checksum = "0fcc0b4a115bf80b728eb8ea024ad5bd707b615bfed49e0665b6e0f86fd082d9"
 
 [[package]]
 name = "httpdate"
@@ -1542,15 +1665,15 @@ checksum = "df3b46402a9d5adb4c86a0cf463f42e19994e3ee891101b1841f30a545cb49a9"
 
 [[package]]
 name = "hyper"
-version = "0.14.28"
+version = "0.14.29"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "bf96e135eb83a2a8ddf766e426a841d8ddd7449d5f00d34ea02b41d2f19eef80"
+checksum = "f361cde2f109281a220d4307746cdfd5ee3f410da58a70377762396775634b33"
 dependencies = [
  "bytes",
  "futures-channel",
  "futures-core",
  "futures-util",
- "h2",
+ "h2 0.3.26",
  "http 0.2.12",
  "http-body 0.4.6",
  "httparse",
@@ -1566,13 +1689,14 @@ dependencies = [
 
 [[package]]
 name = "hyper"
-version = "1.3.1"
+version = "1.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fe575dd17d0862a9a33781c8c4696a55c320909004a67a00fb286ba8b1bc496d"
+checksum = "c4fe55fb7a772d59a5ff1dfbff4fe0258d19b89fec4b233e75d35d5d2316badc"
 dependencies = [
  "bytes",
  "futures-channel",
  "futures-util",
+ "h2 0.4.5",
  "http 1.1.0",
  "http-body 1.0.0",
  "httparse",
@@ -1581,6 +1705,27 @@ dependencies = [
  "pin-project-lite",
  "smallvec",
  "tokio",
+ "want",
+]
+
+[[package]]
+name = "hyper-rustls"
+version = "0.27.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5ee4be2c948921a1a5320b629c4193916ed787a7f7f293fd3f7f5a6c9de74155"
+dependencies = [
+ "futures-util",
+ "http 1.1.0",
+ "hyper 1.4.0",
+ "hyper-util",
+ "log",
+ "rustls 0.23.10",
+ "rustls-native-certs",
+ "rustls-pki-types",
+ "tokio",
+ "tokio-rustls",
+ "tower-service",
+ "webpki-roots",
 ]
 
 [[package]]
@@ -1589,7 +1734,7 @@ version = "0.4.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "bbb958482e8c7be4bc3cf272a766a2b0bf1a6755e7a6ae777f017a31d11b13b1"
 dependencies = [
- "hyper 0.14.28",
+ "hyper 0.14.29",
  "pin-project-lite",
  "tokio",
  "tokio-io-timeout",
@@ -1602,26 +1747,46 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "d6183ddfa99b85da61a140bea0efc93fdf56ceaa041b37d553518030827f9905"
 dependencies = [
  "bytes",
- "hyper 0.14.28",
+ "hyper 0.14.29",
  "native-tls",
  "tokio",
  "tokio-native-tls",
 ]
 
+[[package]]
+name = "hyper-tls"
+version = "0.6.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "70206fc6890eaca9fde8a0bf71caa2ddfc9fe045ac9e5c70df101a7dbde866e0"
+dependencies = [
+ "bytes",
+ "http-body-util",
+ "hyper 1.4.0",
+ "hyper-util",
+ "native-tls",
+ "tokio",
+ "tokio-native-tls",
+ "tower-service",
+]
+
 [[package]]
 name = "hyper-util"
-version = "0.1.3"
+version = "0.1.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ca38ef113da30126bbff9cd1705f9273e15d45498615d138b0c20279ac7a76aa"
+checksum = "3ab92f4f49ee4fb4f997c784b7a2e0fa70050211e0b6a287f898c3c9785ca956"
 dependencies = [
  "bytes",
+ "futures-channel",
  "futures-util",
  "http 1.1.0",
  "http-body 1.0.0",
- "hyper 1.3.1",
+ "hyper 1.4.0",
  "pin-project-lite",
  "socket2",
  "tokio",
+ "tower",
+ "tower-service",
+ "tracing",
 ]
 
 [[package]]
@@ -1680,7 +1845,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "168fb715dda47215e360912c096649d23d58bf392ac62f73919e831745e40f26"
 dependencies = [
  "equivalent",
- "hashbrown 0.14.3",
+ "hashbrown 0.14.5",
  "serde",
 ]
 
@@ -1699,15 +1864,16 @@ dependencies = [
 
 [[package]]
 name = "init-tracing-opentelemetry"
-version = "0.14.1"
+version = "0.18.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "94bd26b1b737bc11f183620072e188d1c6ede67e0e78682228d66b49ec510e17"
+checksum = "754367a7e7f9314afe3ee9780ba8b8ea2cab487cdc8017c9794793051242f878"
 dependencies = [
- "opentelemetry 0.20.0",
- "opentelemetry-otlp",
+ "opentelemetry 0.22.0",
+ "opentelemetry-otlp 0.15.0",
+ "opentelemetry_sdk 0.22.1",
  "thiserror",
  "tracing",
- "tracing-opentelemetry 0.21.0",
+ "tracing-opentelemetry 0.23.0",
 ]
 
 [[package]]
@@ -1726,9 +1892,9 @@ dependencies = [
 
 [[package]]
 name = "instant"
-version = "0.1.12"
+version = "0.1.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7a5bbe824c507c5da5956355e86a746d82e0e1464f65d862cc5e71da70e94b2c"
+checksum = "e0242819d153cba4b4b05a5a8f2a7e9bbf97b6055b2a002b395c96b5ff3c0222"
 dependencies = [
  "cfg-if",
 ]
@@ -1770,6 +1936,12 @@ dependencies = [
  "num-traits",
 ]
 
+[[package]]
+name = "is_terminal_polyfill"
+version = "1.70.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f8478577c03552c21db0e2724ffb8986a5ce7af88107e6be5d2ee6e158c12800"
+
 [[package]]
 name = "itertools"
 version = "0.10.5"
@@ -1803,6 +1975,15 @@ version = "1.0.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "49f1f14873335454500d59611f1cf4a4b0f786f9ac11f4312a78e4cf2566695b"
 
+[[package]]
+name = "jobserver"
+version = "0.1.31"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d2b099aaa34a9751c5bf0878add70444e1ed2dd73f347be99003d4577277de6e"
+dependencies = [
+ "libc",
+]
+
 [[package]]
 name = "js-sys"
 version = "0.3.69"
@@ -1814,15 +1995,31 @@ dependencies = [
 
 [[package]]
 name = "lazy_static"
-version = "1.4.0"
+version = "1.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe"
+
+[[package]]
+name = "lazycell"
+version = "1.3.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e2abad23fbc42b3700f2f279844dc832adb2b2eb069b2df918f455c4e18cc646"
+checksum = "830d08ce1d1d941e6b30645f1a0eb5643013d835ce3779a5fc208261dbe10f55"
 
 [[package]]
 name = "libc"
-version = "0.2.153"
+version = "0.2.155"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "97b3888a4aecf77e811145cadf6eef5901f4782c53886191b2f693f24761847c"
+
+[[package]]
+name = "libloading"
+version = "0.8.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9c198f91728a82281a64e1f4f9eeb25d82cb32a5de251c6bd1b5154d63a8e7bd"
+checksum = "e310b3a6b5907f99202fcdb4960ff45b93735d7c7d96b760fcff8db2dc0e103d"
+dependencies = [
+ "cfg-if",
+ "windows-targets 0.52.5",
+]
 
 [[package]]
 name = "libm"
@@ -1832,9 +2029,9 @@ checksum = "4ec2a862134d2a7d32d7983ddcdd1c4923530833c9f2ea1a44fc5fa473989058"
 
 [[package]]
 name = "libmimalloc-sys"
-version = "0.1.35"
+version = "0.1.39"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3979b5c37ece694f1f5e51e7ecc871fdb0f517ed04ee45f88d15d6d553cb9664"
+checksum = "23aa6811d3bd4deb8a84dde645f943476d13b248d818edcf8ce0b2f37f036b44"
 dependencies = [
  "cc",
  "libc",
@@ -1846,7 +2043,7 @@ version = "0.1.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c0ff37bd590ca25063e35af745c343cb7a0271906fb7b37e4813e8f79f00268d"
 dependencies = [
- "bitflags 2.5.0",
+ "bitflags 2.6.0",
  "libc",
 ]
 
@@ -1858,15 +2055,15 @@ checksum = "0717cef1bc8b636c6e1c1bbdefc09e6322da8a9321966e8928ef80d20f7f770f"
 
 [[package]]
 name = "linux-raw-sys"
-version = "0.4.13"
+version = "0.4.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "01cda141df6706de531b6c46c3a33ecca755538219bd484262fa09410c13539c"
+checksum = "78b3ae25bc7c8c38cec158d1f2757ee79e9b3740fbc7ccf0e59e4b08d793fa89"
 
 [[package]]
 name = "lock_api"
-version = "0.4.11"
+version = "0.4.12"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3c168f8615b12bc01f9c17e2eb0cc07dcae1940121185446edc3744920e8ef45"
+checksum = "07af8b9cdd281b7915f413fa73f29ebd5d55d0d3f0155584dade1ff18cea1b17"
 dependencies = [
  "autocfg",
  "scopeguard",
@@ -1874,18 +2071,9 @@ dependencies = [
 
 [[package]]
 name = "log"
-version = "0.4.21"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "90ed8c1e510134f979dbc4f070f87d4313098b704861a105fe34231c70a3901c"
-
-[[package]]
-name = "mach2"
-version = "0.4.2"
+version = "0.4.22"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "19b955cdeb2a02b9117f121ce63aa52d08ade45de53e48fe6a38b39c10f6f709"
-dependencies = [
- "libc",
-]
+checksum = "a7a70ba024b9dc04c27ea2f0c0548feb474ec5c54bba33a7f72f873a39d07b24"
 
 [[package]]
 name = "macro_rules_attribute"
@@ -1929,9 +2117,9 @@ checksum = "0e7465ac9959cc2b1404e8e2367b43684a6d13790fe23056cc8c6c5a6b7bcb94"
 
 [[package]]
 name = "memchr"
-version = "2.7.2"
+version = "2.7.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6c8640c5d730cb13ebd907d8d04b52f55ac9a2eec55b440c8892f40d56c76c1d"
+checksum = "78ca9ab1a0babb1e7d5695e3530886289c18cf2f87ec19a575a0abdce112e3a3"
 
 [[package]]
 name = "memmap2"
@@ -1949,7 +2137,7 @@ version = "0.27.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c43f73953f8cbe511f021b58f18c3ce1c3d1ae13fe953293e13345bf83217f25"
 dependencies = [
- "bitflags 2.5.0",
+ "bitflags 2.6.0",
  "block",
  "core-graphics-types",
  "foreign-types 0.5.0",
@@ -1960,24 +2148,26 @@ dependencies = [
 
 [[package]]
 name = "metrics"
-version = "0.21.1"
+version = "0.23.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fde3af1a009ed76a778cb84fdef9e7dbbdf5775ae3e4cc1f434a6a307f6f76c5"
+checksum = "884adb57038347dfbaf2d5065887b6cf4312330dc8e94bc30a1a839bd79d3261"
 dependencies = [
  "ahash",
- "metrics-macros",
  "portable-atomic",
 ]
 
 [[package]]
 name = "metrics-exporter-prometheus"
-version = "0.12.2"
+version = "0.15.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1d4fa7ce7c4862db464a37b0b31d89bca874562f034bd7993895572783d02950"
+checksum = "bf0af7a0d7ced10c0151f870e5e3f3f8bc9ffc5992d32873566ca1f9169ae776"
 dependencies = [
- "base64 0.21.7",
- "hyper 0.14.28",
- "indexmap 1.9.3",
+ "base64 0.22.1",
+ "http-body-util",
+ "hyper 1.4.0",
+ "hyper-rustls",
+ "hyper-util",
+ "indexmap 2.2.6",
  "ipnet",
  "metrics",
  "metrics-util",
@@ -1987,26 +2177,15 @@ dependencies = [
  "tracing",
 ]
 
-[[package]]
-name = "metrics-macros"
-version = "0.7.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "38b4faf00617defe497754acde3024865bc143d44a86799b24e191ecff91354f"
-dependencies = [
- "proc-macro2",
- "quote",
- "syn 2.0.59",
-]
-
 [[package]]
 name = "metrics-util"
-version = "0.15.1"
+version = "0.17.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4de2ed6e491ed114b40b732e4d1659a9d53992ebd87490c44a6ffe23739d973e"
+checksum = "4259040465c955f9f2f1a4a8a16dc46726169bca0f88e8fb2dbeced487c3e828"
 dependencies = [
  "crossbeam-epoch",
  "crossbeam-utils",
- "hashbrown 0.13.1",
+ "hashbrown 0.14.5",
  "metrics",
  "num_cpus",
  "quanta",
@@ -2015,9 +2194,9 @@ dependencies = [
 
 [[package]]
 name = "mimalloc"
-version = "0.1.39"
+version = "0.1.43"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fa01922b5ea280a911e323e4d2fd24b7fe5cc4042e0d2cda3c40775cdc4bdc9c"
+checksum = "68914350ae34959d83f732418d51e2427a794055d0b9529f48259ac07af65633"
 dependencies = [
  "libmimalloc-sys",
 ]
@@ -2030,9 +2209,9 @@ checksum = "6877bb514081ee2a7ff5ef9de3281f14a4dd4bceac4c09388074a6b5df8a139a"
 
 [[package]]
 name = "mime_guess"
-version = "2.0.4"
+version = "2.0.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4192263c238a5f0d0c6bfd21f336a313a4ce1c450542449ca191bb657b4642ef"
+checksum = "f7c44f8e672c00fe5308fa235f821cb4198414e1c77935c1ab6948d3fd78550e"
 dependencies = [
  "mime",
  "unicase",
@@ -2046,9 +2225,9 @@ checksum = "68354c5c6bd36d73ff3feceb05efa59b6acb7626617f4962be322a825e61f79a"
 
 [[package]]
 name = "miniz_oxide"
-version = "0.7.2"
+version = "0.7.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9d811f3e15f28568be3407c8e7fdb6514c1cda3cb30683f15b6a1a1dc4ea14a7"
+checksum = "b8a240ddb74feaf34a79a7add65a741f3167852fba007066dcac1ca548d89c08"
 dependencies = [
  "adler",
 ]
@@ -2064,11 +2243,17 @@ dependencies = [
  "windows-sys 0.48.0",
 ]
 
+[[package]]
+name = "mirai-annotations"
+version = "1.12.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c9be0862c1b3f26a88803c4a49de6889c10e608b3ee9344e6ef5b45fb37ad3d1"
+
 [[package]]
 name = "monostate"
-version = "0.1.12"
+version = "0.1.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a20fffcd8ca4c69d31e036a71abc400147b41f90895df4edcb36497a1f8af8bf"
+checksum = "0d208407d7552cd041d8cdb69a1bc3303e029c598738177a3d87082004dc0e1e"
 dependencies = [
  "monostate-impl",
  "serde",
@@ -2076,13 +2261,13 @@ dependencies = [
 
 [[package]]
 name = "monostate-impl"
-version = "0.1.12"
+version = "0.1.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "bf307cbbbd777a9c10cec88ddafee572b3484caad5cce0c9236523c3803105a6"
+checksum = "a7ce64b975ed4f123575d11afd9491f2e37bbd5813fbfbc0f09ae1fbddea74e0"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -2099,11 +2284,10 @@ checksum = "defc4c55412d89136f966bbb339008b474350e5e6e78d2714439c386b3137a03"
 
 [[package]]
 name = "native-tls"
-version = "0.2.11"
+version = "0.2.12"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "07226173c32f2926027b63cce4bcd8076c3552846cbe7925f3aaffeac0a3b92e"
+checksum = "a8614eb2c83d59d1c8cc974dd3f920198647674a0a035e1af1fa58707e317466"
 dependencies = [
- "lazy_static",
  "libc",
  "log",
  "openssl",
@@ -2143,9 +2327,9 @@ dependencies = [
 
 [[package]]
 name = "num-complex"
-version = "0.4.5"
+version = "0.4.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "23c6602fda94a57c990fe0df199a035d83576b496aa29f4e634a8ac6004e68a6"
+checksum = "73f88a1307638156682bada9d7604135552957b7818057dcef22705b4d509495"
 dependencies = [
  "bytemuck",
  "num-traits",
@@ -2159,9 +2343,9 @@ checksum = "51d515d32fb182ee37cda2ccdcb92950d6a3c2893aa280e540671c2cd0f3b1d9"
 
 [[package]]
 name = "num-traits"
-version = "0.2.18"
+version = "0.2.19"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "da0df0e5185db44f69b44f26786fe401b6c293d1907744beaa7fa62b2e5a517a"
+checksum = "071dfc062690e90b734c0b2273ce72ad0ffa95f0c74596bc250dcfd960262841"
 dependencies = [
  "autocfg",
  "libm",
@@ -2177,6 +2361,27 @@ dependencies = [
  "libc",
 ]
 
+[[package]]
+name = "num_enum"
+version = "0.7.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "02339744ee7253741199f897151b38e72257d13802d4ee837285cc2990a90845"
+dependencies = [
+ "num_enum_derive",
+]
+
+[[package]]
+name = "num_enum_derive"
+version = "0.7.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "681030a937600a36906c185595136d26abfebb4aa9c65701cefcaf8578bb982b"
+dependencies = [
+ "proc-macro-crate",
+ "proc-macro2",
+ "quote",
+ "syn 2.0.68",
+]
+
 [[package]]
 name = "num_threads"
 version = "0.1.7"
@@ -2213,23 +2418,25 @@ dependencies = [
 
 [[package]]
 name = "object"
-version = "0.32.2"
+version = "0.36.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a6a622008b6e321afc04970976f62ee297fdbaa6f95318ca343e3eebb9648441"
+checksum = "081b846d1d56ddfc18fdf1a922e4f6e07a11768ea1b92dec44e42b72712ccfce"
 dependencies = [
  "memchr",
 ]
 
 [[package]]
 name = "oci-spec"
-version = "0.6.5"
+version = "0.6.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e423c4f827362c0d8d8da4b1f571270f389ebde73bcd3240a3d23c6d6f61d0f0"
+checksum = "098bc31d7c87110e22090eb082903caaed19f06319f63b6f7d7ed273bc8bdaec"
 dependencies = [
- "derive_builder 0.20.0",
+ "derive_builder",
  "getset",
  "serde",
  "serde_json",
+ "strum",
+ "strum_macros",
  "thiserror",
 ]
 
@@ -2240,7 +2447,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "9bb3293021f06540803301af45e7ab81693d50e89a7398a3420bdab139e7ba5e"
 dependencies = [
  "base16ct",
- "base64 0.22.0",
+ "base64 0.22.1",
  "chrono",
  "directories",
  "flate2",
@@ -2294,7 +2501,7 @@ version = "0.10.64"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "95a0481286a310808298130d22dd1fef0fa571e05a8f44ec801801e84b216b1f"
 dependencies = [
- "bitflags 2.5.0",
+ "bitflags 2.6.0",
  "cfg-if",
  "foreign-types 0.3.2",
  "libc",
@@ -2311,7 +2518,7 @@ checksum = "a948666b637a0f465e8564c73e89d4dde00d72d4d473cc972f390fc3dcee7d9c"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -2338,102 +2545,116 @@ version = "0.19.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "5f4b8347cc26099d3aeee044065ecc3ae11469796b4d65d065a23a584ed92a6f"
 dependencies = [
- "opentelemetry_api 0.19.0",
+ "opentelemetry_api",
  "opentelemetry_sdk 0.19.0",
 ]
 
 [[package]]
 name = "opentelemetry"
-version = "0.20.0"
+version = "0.22.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9591d937bc0e6d2feb6f71a559540ab300ea49955229c347a517a28d27784c54"
+checksum = "900d57987be3f2aeb70d385fff9b27fb74c5723cc9a52d904d4f9c807a0667bf"
 dependencies = [
- "opentelemetry_api 0.20.0",
- "opentelemetry_sdk 0.20.0",
+ "futures-core",
+ "futures-sink",
+ "js-sys",
+ "once_cell",
+ "pin-project-lite",
+ "thiserror",
+ "urlencoding",
 ]
 
 [[package]]
 name = "opentelemetry"
-version = "0.21.0"
+version = "0.23.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1e32339a5dc40459130b3bd269e9892439f55b33e772d2a9d402a789baaf4e8a"
+checksum = "1b69a91d4893e713e06f724597ad630f1fa76057a5e1026c0ca67054a9032a76"
 dependencies = [
  "futures-core",
  "futures-sink",
- "indexmap 2.2.6",
  "js-sys",
  "once_cell",
  "pin-project-lite",
  "thiserror",
- "urlencoding",
 ]
 
 [[package]]
 name = "opentelemetry-otlp"
-version = "0.13.0"
+version = "0.15.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7e5e5a5c4135864099f3faafbe939eb4d7f9b80ebf68a8448da961b32a7c1275"
+checksum = "1a016b8d9495c639af2145ac22387dcb88e44118e45320d9238fbf4e7889abcb"
 dependencies = [
  "async-trait",
  "futures-core",
  "http 0.2.12",
- "opentelemetry-proto",
+ "opentelemetry 0.22.0",
+ "opentelemetry-proto 0.5.0",
  "opentelemetry-semantic-conventions",
- "opentelemetry_api 0.20.0",
- "opentelemetry_sdk 0.20.0",
- "prost 0.11.9",
+ "opentelemetry_sdk 0.22.1",
+ "prost 0.12.6",
  "thiserror",
  "tokio",
- "tonic 0.9.2",
+ "tonic 0.11.0",
 ]
 
 [[package]]
-name = "opentelemetry-proto"
-version = "0.3.0"
+name = "opentelemetry-otlp"
+version = "0.16.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b1e3f814aa9f8c905d0ee4bde026afd3b2577a97c10e1699912e3e44f0c4cbeb"
+checksum = "a94c69209c05319cdf7460c6d4c055ed102be242a0a6245835d7bc42c6ec7f54"
 dependencies = [
- "opentelemetry_api 0.20.0",
- "opentelemetry_sdk 0.20.0",
- "prost 0.11.9",
- "tonic 0.9.2",
+ "async-trait",
+ "futures-core",
+ "http 0.2.12",
+ "opentelemetry 0.23.0",
+ "opentelemetry-proto 0.6.0",
+ "opentelemetry_sdk 0.23.0",
+ "prost 0.12.6",
+ "thiserror",
+ "tokio",
+ "tonic 0.11.0",
 ]
 
 [[package]]
-name = "opentelemetry-semantic-conventions"
-version = "0.12.0"
+name = "opentelemetry-proto"
+version = "0.5.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "73c9f9340ad135068800e7f1b24e9e09ed9e7143f5bf8518ded3d3ec69789269"
+checksum = "3a8fddc9b68f5b80dae9d6f510b88e02396f006ad48cac349411fbecc80caae4"
 dependencies = [
- "opentelemetry 0.20.0",
+ "opentelemetry 0.22.0",
+ "opentelemetry_sdk 0.22.1",
+ "prost 0.12.6",
+ "tonic 0.11.0",
 ]
 
 [[package]]
-name = "opentelemetry_api"
-version = "0.19.0"
+name = "opentelemetry-proto"
+version = "0.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ed41783a5bf567688eb38372f2b7a8530f5a607a4b49d38dd7573236c23ca7e2"
+checksum = "984806e6cf27f2b49282e2a05e288f30594f3dbc74eb7a6e99422bc48ed78162"
 dependencies = [
- "fnv",
- "futures-channel",
- "futures-util",
- "indexmap 1.9.3",
- "once_cell",
- "pin-project-lite",
- "thiserror",
- "urlencoding",
+ "opentelemetry 0.23.0",
+ "opentelemetry_sdk 0.23.0",
+ "prost 0.12.6",
+ "tonic 0.11.0",
 ]
 
+[[package]]
+name = "opentelemetry-semantic-conventions"
+version = "0.14.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f9ab5bd6c42fb9349dcf28af2ba9a0667f697f9bdcca045d39f2cec5543e2910"
+
 [[package]]
 name = "opentelemetry_api"
-version = "0.20.0"
+version = "0.19.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8a81f725323db1b1206ca3da8bb19874bbd3f57c3bcd59471bfb04525b265b9b"
+checksum = "ed41783a5bf567688eb38372f2b7a8530f5a607a4b49d38dd7573236c23ca7e2"
 dependencies = [
+ "fnv",
  "futures-channel",
  "futures-util",
  "indexmap 1.9.3",
- "js-sys",
  "once_cell",
  "pin-project-lite",
  "thiserror",
@@ -2454,7 +2675,7 @@ dependencies = [
  "futures-executor",
  "futures-util",
  "once_cell",
- "opentelemetry_api 0.19.0",
+ "opentelemetry_api",
  "percent-encoding",
  "rand",
  "thiserror",
@@ -2462,22 +2683,21 @@ dependencies = [
 
 [[package]]
 name = "opentelemetry_sdk"
-version = "0.20.0"
+version = "0.22.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fa8e705a0612d48139799fcbaba0d4a90f06277153e43dd2bdc16c6f0edd8026"
+checksum = "9e90c7113be649e31e9a0f8b5ee24ed7a16923b322c3c5ab6367469c049d6b7e"
 dependencies = [
  "async-trait",
  "crossbeam-channel",
  "futures-channel",
  "futures-executor",
  "futures-util",
+ "glob",
  "once_cell",
- "opentelemetry_api 0.20.0",
- "ordered-float 3.9.2",
+ "opentelemetry 0.22.0",
+ "ordered-float",
  "percent-encoding",
  "rand",
- "regex",
- "serde_json",
  "thiserror",
  "tokio",
  "tokio-stream",
@@ -2485,22 +2705,24 @@ dependencies = [
 
 [[package]]
 name = "opentelemetry_sdk"
-version = "0.21.2"
+version = "0.23.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2f16aec8a98a457a52664d69e0091bac3a0abd18ead9b641cb00202ba4e0efe4"
+checksum = "ae312d58eaa90a82d2e627fd86e075cf5230b3f11794e2ed74199ebbe572d4fd"
 dependencies = [
  "async-trait",
- "crossbeam-channel",
  "futures-channel",
  "futures-executor",
  "futures-util",
  "glob",
+ "lazy_static",
  "once_cell",
- "opentelemetry 0.21.0",
- "ordered-float 4.2.0",
+ "opentelemetry 0.23.0",
+ "ordered-float",
  "percent-encoding",
  "rand",
  "thiserror",
+ "tokio",
+ "tokio-stream",
 ]
 
 [[package]]
@@ -2511,18 +2733,9 @@ checksum = "04744f49eae99ab78e0d5c0b603ab218f515ea8cfe5a456d7629ad883a3b6e7d"
 
 [[package]]
 name = "ordered-float"
-version = "3.9.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f1e1c390732d15f1d48471625cd92d154e66db2c56645e29a9cd26f4699f72dc"
-dependencies = [
- "num-traits",
-]
-
-[[package]]
-name = "ordered-float"
-version = "4.2.0"
+version = "4.2.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a76df7075c7d4d01fdcb46c912dd17fba5b60c78ea480b475f2b6ab6f666584e"
+checksum = "19ff2cf528c6c03d9ed653d6c4ce1dc0582dc4af309790ad92f07c1cd551b0be"
 dependencies = [
  "num-traits",
 ]
@@ -2533,11 +2746,17 @@ version = "0.1.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b15813163c1d831bf4a13c3610c05c0d03b39feb07f7e09fa234dac9b15aaf39"
 
+[[package]]
+name = "parking"
+version = "2.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bb813b8af86854136c6922af0598d719255ecb2179515e6e7730d468f05c9cae"
+
 [[package]]
 name = "parking_lot"
-version = "0.12.1"
+version = "0.12.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3742b2c103b9f06bc9fff0a37ff4912935851bee6d36f3c02bcc755bcfec228f"
+checksum = "f1bf18183cf54e8d6059647fc3063646a1801cf30896933ec2311622cc4b9a27"
 dependencies = [
  "lock_api",
  "parking_lot_core",
@@ -2545,22 +2764,22 @@ dependencies = [
 
 [[package]]
 name = "parking_lot_core"
-version = "0.9.9"
+version = "0.9.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4c42a9226546d68acdd9c0a280d17ce19bfe27a46bf68784e4066115788d008e"
+checksum = "1e401f977ab385c9e4e3ab30627d6f26d00e2c73eef317493c4ec6d468726cf8"
 dependencies = [
  "cfg-if",
  "libc",
- "redox_syscall",
+ "redox_syscall 0.5.2",
  "smallvec",
- "windows-targets 0.48.5",
+ "windows-targets 0.52.5",
 ]
 
 [[package]]
 name = "paste"
-version = "1.0.14"
+version = "1.0.15"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "de3145af08024dea9fa9914f381a17b8fc6034dfb00f3a84013f7ff43f29ed4c"
+checksum = "57c0d7b74b563b49d38dae00a0c37d4d6de9b432382b2892f0574ddcae73fd0a"
 
 [[package]]
 name = "percent-encoding"
@@ -2570,9 +2789,9 @@ checksum = "e3148f5046208a5d56bcfc03053e3ca6334e51da8dfb19b6cdc8b306fae3283e"
 
 [[package]]
 name = "petgraph"
-version = "0.6.4"
+version = "0.6.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e1d3afd2628e69da2be385eb6f2fd57c8ac7977ceeff6dc166ff1657b0e386a9"
+checksum = "b4c5cc86750666a3ed20bdaf5ca2a0344f9c67674cae0515bec2da16fbaa47db"
 dependencies = [
  "fixedbitset",
  "indexmap 2.2.6",
@@ -2595,7 +2814,7 @@ checksum = "2f38a4412a78282e09a2cf38d195ea5420d15ba0602cb375210efbc877243965"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -2646,12 +2865,21 @@ dependencies = [
 
 [[package]]
 name = "prettyplease"
-version = "0.2.19"
+version = "0.2.20"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5ac2cf0f2e4f42b49f5ffd07dae8d746508ef7526c13940e5f524012ae6c6550"
+checksum = "5f12335488a2f3b0a83b14edad48dca9879ce89b2edd10e80237e4e852dd645e"
 dependencies = [
  "proc-macro2",
- "syn 2.0.59",
+ "syn 2.0.68",
+]
+
+[[package]]
+name = "proc-macro-crate"
+version = "3.1.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6d37c51ca738a55da99dc0c4a34860fd675453b8b36209178c2249bb13651284"
+dependencies = [
+ "toml_edit 0.21.1",
 ]
 
 [[package]]
@@ -2680,9 +2908,9 @@ dependencies = [
 
 [[package]]
 name = "proc-macro2"
-version = "1.0.80"
+version = "1.0.86"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a56dea16b0a29e94408b9aa5e2940a4eedbd128a1ba20e8f7ae60fd3d465af0e"
+checksum = "5e719e8df665df0d1c8fbfd238015744736151d4445ec0836b8e628aae103b77"
 dependencies = [
  "unicode-ident",
 ]
@@ -2699,12 +2927,12 @@ dependencies = [
 
 [[package]]
 name = "prost"
-version = "0.12.4"
+version = "0.12.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d0f5d036824e4761737860779c906171497f6d55681139d8312388f8fe398922"
+checksum = "deb1435c188b76130da55f17a466d252ff7b1418b2ad3e037d127b94e3411f29"
 dependencies = [
  "bytes",
- "prost-derive 0.12.4",
+ "prost-derive 0.12.6",
 ]
 
 [[package]]
@@ -2731,9 +2959,9 @@ dependencies = [
 
 [[package]]
 name = "prost-build"
-version = "0.12.4"
+version = "0.12.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "80b776a1b2dc779f5ee0641f8ade0125bc1298dd41a9a0c16d8bd57b42d222b1"
+checksum = "22505a5c94da8e3b7c2996394d1c933236c4d743e81a410bcca4e6989fc066a4"
 dependencies = [
  "bytes",
  "heck 0.5.0",
@@ -2742,11 +2970,11 @@ dependencies = [
  "multimap 0.10.0",
  "once_cell",
  "petgraph",
- "prettyplease 0.2.19",
- "prost 0.12.4",
- "prost-types 0.12.4",
+ "prettyplease 0.2.20",
+ "prost 0.12.6",
+ "prost-types 0.12.6",
  "regex",
- "syn 2.0.59",
+ "syn 2.0.68",
  "tempfile",
 ]
 
@@ -2765,15 +2993,15 @@ dependencies = [
 
 [[package]]
 name = "prost-derive"
-version = "0.12.4"
+version = "0.12.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "19de2de2a00075bf566bee3bd4db014b11587e84184d3f7a791bc17f1a8e9e48"
+checksum = "81bddcdb20abf9501610992b6759a4c888aef7d1a7247ef75e2404275ac24af1"
 dependencies = [
  "anyhow",
  "itertools 0.12.1",
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -2787,18 +3015,18 @@ dependencies = [
 
 [[package]]
 name = "prost-types"
-version = "0.12.4"
+version = "0.12.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3235c33eb02c1f1e212abdbe34c78b264b038fb58ca612664343271e36e55ffe"
+checksum = "9091c90b0a32608e984ff2fa4091273cbdd755d54935c51d520887f4a1dbd5b0"
 dependencies = [
- "prost 0.12.4",
+ "prost 0.12.6",
 ]
 
 [[package]]
 name = "pulp"
-version = "0.18.10"
+version = "0.18.21"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e14989307e408d9f4245d4fda09a7b144a08114ba124e26cab60ab83dc98db10"
+checksum = "0ec8d02258294f59e4e223b41ad7e81c874aa6b15bc4ced9ba3965826da0eed5"
 dependencies = [
  "bytemuck",
  "libm",
@@ -2808,20 +3036,66 @@ dependencies = [
 
 [[package]]
 name = "quanta"
-version = "0.11.1"
+version = "0.12.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a17e662a7a8291a865152364c20c7abc5e60486ab2001e8ec10b24862de0b9ab"
+checksum = "8e5167a477619228a0b284fac2674e3c388cba90631d7b7de620e6f1fcd08da5"
 dependencies = [
  "crossbeam-utils",
  "libc",
- "mach2",
  "once_cell",
- "raw-cpuid",
+ "raw-cpuid 11.0.2",
  "wasi",
  "web-sys",
  "winapi",
 ]
 
+[[package]]
+name = "quinn"
+version = "0.11.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e4ceeeeabace7857413798eb1ffa1e9c905a9946a57d81fb69b4b71c4d8eb3ad"
+dependencies = [
+ "bytes",
+ "pin-project-lite",
+ "quinn-proto",
+ "quinn-udp",
+ "rustc-hash",
+ "rustls 0.23.10",
+ "thiserror",
+ "tokio",
+ "tracing",
+]
+
+[[package]]
+name = "quinn-proto"
+version = "0.11.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ddf517c03a109db8100448a4be38d498df8a210a99fe0e1b9eaf39e78c640efe"
+dependencies = [
+ "bytes",
+ "rand",
+ "ring",
+ "rustc-hash",
+ "rustls 0.23.10",
+ "slab",
+ "thiserror",
+ "tinyvec",
+ "tracing",
+]
+
+[[package]]
+name = "quinn-udp"
+version = "0.5.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9096629c45860fc7fb143e125eb826b5e721e10be3263160c7d60ca832cf8c46"
+dependencies = [
+ "libc",
+ "once_cell",
+ "socket2",
+ "tracing",
+ "windows-sys 0.52.0",
+]
+
 [[package]]
 name = "quote"
 version = "1.0.36"
@@ -2880,6 +3154,15 @@ dependencies = [
  "bitflags 1.3.2",
 ]
 
+[[package]]
+name = "raw-cpuid"
+version = "11.0.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e29830cbb1290e404f24c73af91c5d8d631ce7e128691e9477556b540cd01ecd"
+dependencies = [
+ "bitflags 2.6.0",
+]
+
 [[package]]
 name = "rayon"
 version = "1.10.0"
@@ -2926,6 +3209,15 @@ dependencies = [
  "bitflags 1.3.2",
 ]
 
+[[package]]
+name = "redox_syscall"
+version = "0.5.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c82cf8cff14456045f55ec4241383baeff27af886adb72ffb2162f99911de0fd"
+dependencies = [
+ "bitflags 2.6.0",
+]
+
 [[package]]
 name = "redox_users"
 version = "0.4.5"
@@ -2939,14 +3231,14 @@ dependencies = [
 
 [[package]]
 name = "regex"
-version = "1.10.4"
+version = "1.10.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c117dbdfde9c8308975b6a18d71f3f385c89461f7b3fb054288ecf2a2058ba4c"
+checksum = "b91213439dad192326a0d7c6ee3955910425f441d7038e0d6933b0aec5c4517f"
 dependencies = [
  "aho-corasick",
  "memchr",
- "regex-automata 0.4.6",
- "regex-syntax 0.8.3",
+ "regex-automata 0.4.7",
+ "regex-syntax 0.8.4",
 ]
 
 [[package]]
@@ -2960,13 +3252,13 @@ dependencies = [
 
 [[package]]
 name = "regex-automata"
-version = "0.4.6"
+version = "0.4.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "86b83b8b9847f9bf95ef68afb0b8e6cdb80f498442f5179a29fad448fcc1eaea"
+checksum = "38caf58cc5ef2fed281f89292ef23f6365465ed9a41b7a7754eb4e26496c92df"
 dependencies = [
  "aho-corasick",
  "memchr",
- "regex-syntax 0.8.3",
+ "regex-syntax 0.8.4",
 ]
 
 [[package]]
@@ -2977,9 +3269,9 @@ checksum = "f162c6dd7b008981e4d40210aca20b4bd0f9b60ca9271061b07f78537722f2e1"
 
 [[package]]
 name = "regex-syntax"
-version = "0.8.3"
+version = "0.8.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "adad44e29e4c806119491a7f06f03de4d1af22c3a680dd47f1e6e179439d1f56"
+checksum = "7a66a03ae7c801facd77a29370b4faec201768915ac14a721ba36f20bc9c209b"
 
 [[package]]
 name = "reqwest"
@@ -2992,11 +3284,11 @@ dependencies = [
  "encoding_rs",
  "futures-core",
  "futures-util",
- "h2",
+ "h2 0.3.26",
  "http 0.2.12",
  "http-body 0.4.6",
- "hyper 0.14.28",
- "hyper-tls",
+ "hyper 0.14.29",
+ "hyper-tls 0.5.0",
  "ipnet",
  "js-sys",
  "log",
@@ -3005,7 +3297,7 @@ dependencies = [
  "once_cell",
  "percent-encoding",
  "pin-project-lite",
- "rustls-pemfile",
+ "rustls-pemfile 1.0.4",
  "serde",
  "serde_json",
  "serde_urlencoded",
@@ -3018,7 +3310,56 @@ dependencies = [
  "wasm-bindgen",
  "wasm-bindgen-futures",
  "web-sys",
- "winreg",
+ "winreg 0.50.0",
+]
+
+[[package]]
+name = "reqwest"
+version = "0.12.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c7d6d2a27d57148378eb5e111173f4276ad26340ecc5c49a4a2152167a2d6a37"
+dependencies = [
+ "base64 0.22.1",
+ "bytes",
+ "encoding_rs",
+ "futures-channel",
+ "futures-core",
+ "futures-util",
+ "h2 0.4.5",
+ "http 1.1.0",
+ "http-body 1.0.0",
+ "http-body-util",
+ "hyper 1.4.0",
+ "hyper-rustls",
+ "hyper-tls 0.6.0",
+ "hyper-util",
+ "ipnet",
+ "js-sys",
+ "log",
+ "mime",
+ "native-tls",
+ "once_cell",
+ "percent-encoding",
+ "pin-project-lite",
+ "quinn",
+ "rustls 0.23.10",
+ "rustls-pemfile 2.1.2",
+ "rustls-pki-types",
+ "serde",
+ "serde_json",
+ "serde_urlencoded",
+ "sync_wrapper 1.0.1",
+ "system-configuration",
+ "tokio",
+ "tokio-native-tls",
+ "tokio-rustls",
+ "tower-service",
+ "url",
+ "wasm-bindgen",
+ "wasm-bindgen-futures",
+ "web-sys",
+ "webpki-roots",
+ "winreg 0.52.0",
 ]
 
 [[package]]
@@ -3038,9 +3379,9 @@ dependencies = [
 
 [[package]]
 name = "rust-embed"
-version = "8.3.0"
+version = "8.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fb78f46d0066053d16d4ca7b898e9343bc3530f71c61d5ad84cd404ada068745"
+checksum = "19549741604902eb99a7ed0ee177a0663ee1eda51a29f71401f166e47e77806a"
 dependencies = [
  "rust-embed-impl",
  "rust-embed-utils",
@@ -3049,22 +3390,22 @@ dependencies = [
 
 [[package]]
 name = "rust-embed-impl"
-version = "8.3.0"
+version = "8.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b91ac2a3c6c0520a3fb3dd89321177c3c692937c4eb21893378219da10c44fc8"
+checksum = "cb9f96e283ec64401f30d3df8ee2aaeb2561f34c824381efa24a35f79bf40ee4"
 dependencies = [
  "proc-macro2",
  "quote",
  "rust-embed-utils",
- "syn 2.0.59",
+ "syn 2.0.68",
  "walkdir",
 ]
 
 [[package]]
 name = "rust-embed-utils"
-version = "8.3.0"
+version = "8.4.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "86f69089032567ffff4eada41c573fc43ff466c7db7c5688b2e7969584345581"
+checksum = "38c74a686185620830701348de757fd36bef4aa9680fd23c49fc539ddcc1af32"
 dependencies = [
  "sha2",
  "walkdir",
@@ -3072,17 +3413,23 @@ dependencies = [
 
 [[package]]
 name = "rustc-demangle"
-version = "0.1.23"
+version = "0.1.24"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "719b953e2095829ee67db738b3bfa9fa368c94900df327b3f07fe6e794d2fe1f"
+
+[[package]]
+name = "rustc-hash"
+version = "1.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d626bb9dae77e28219937af045c257c28bfd3f69333c512553507f5f9798cb76"
+checksum = "08d43f7aa6b08d49f382cde6a7982047c3426db949b1424bc4b7ec9ae12c6ce2"
 
 [[package]]
 name = "rustix"
-version = "0.38.32"
+version = "0.38.34"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "65e04861e65f21776e67888bfbea442b3642beaa0138fdb1dd7a84a52dffdb89"
+checksum = "70dc5ec042f7a43c4a73241207cecc9873a06d45debb38b329f8541d85c2730f"
 dependencies = [
- "bitflags 2.5.0",
+ "bitflags 2.6.0",
  "errno",
  "libc",
  "linux-raw-sys",
@@ -3091,11 +3438,27 @@ dependencies = [
 
 [[package]]
 name = "rustls"
-version = "0.22.3"
+version = "0.22.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bf4ef73721ac7bcd79b2b315da7779d8fc09718c6b3d2d1b2d94850eb8c18432"
+dependencies = [
+ "log",
+ "ring",
+ "rustls-pki-types",
+ "rustls-webpki",
+ "subtle",
+ "zeroize",
+]
+
+[[package]]
+name = "rustls"
+version = "0.23.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "99008d7ad0bbbea527ec27bddbc0e432c5b87d8175178cee68d2eec9c4a1813c"
+checksum = "05cff451f60db80f490f3c182b77c35260baace73209e9cdbbe526bfe3a4d402"
 dependencies = [
+ "aws-lc-rs",
  "log",
+ "once_cell",
  "ring",
  "rustls-pki-types",
  "rustls-webpki",
@@ -3103,6 +3466,19 @@ dependencies = [
  "zeroize",
 ]
 
+[[package]]
+name = "rustls-native-certs"
+version = "0.7.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "8f1fb85efa936c42c6d5fc28d2629bb51e4b2f4b8a5211e297d599cc5a093792"
+dependencies = [
+ "openssl-probe",
+ "rustls-pemfile 2.1.2",
+ "rustls-pki-types",
+ "schannel",
+ "security-framework",
+]
+
 [[package]]
 name = "rustls-pemfile"
 version = "1.0.4"
@@ -3112,18 +3488,29 @@ dependencies = [
  "base64 0.21.7",
 ]
 
+[[package]]
+name = "rustls-pemfile"
+version = "2.1.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "29993a25686778eb88d4189742cd713c9bce943bc54251a33509dc63cbacf73d"
+dependencies = [
+ "base64 0.22.1",
+ "rustls-pki-types",
+]
+
 [[package]]
 name = "rustls-pki-types"
-version = "1.4.1"
+version = "1.7.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ecd36cc4259e3e4514335c4a138c6b43171a8d61d8f5c9348f9fc7529416f247"
+checksum = "976295e77ce332211c0d24d92c0e83e50f5c5f046d11082cea19f3df13a3562d"
 
 [[package]]
 name = "rustls-webpki"
-version = "0.102.2"
+version = "0.102.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "faaa0a62740bedb9b2ef5afa303da42764c012f743917351dc9a237ea1663610"
+checksum = "ff448f7e92e913c4b7d4c6d8e4540a1724b319b4152b8aef6d4cf8339712b33e"
 dependencies = [
+ "aws-lc-rs",
  "ring",
  "rustls-pki-types",
  "untrusted",
@@ -3131,15 +3518,15 @@ dependencies = [
 
 [[package]]
 name = "rustversion"
-version = "1.0.15"
+version = "1.0.17"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "80af6f9131f277a45a3fba6ce8e2258037bb0477a67e610d3c1fe046ab31de47"
+checksum = "955d28af4278de8121b7ebeb796b6a45735dc01436d898801014aced2773a3d6"
 
 [[package]]
 name = "ryu"
-version = "1.0.17"
+version = "1.0.18"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e86697c916019a8588c99b5fac3cead74ec0b4b819707a682fd4d23fa0ce1ba1"
+checksum = "f3cb5ba0dc43242ce17de99c180e96db90b235b8a9fdc9543c96d2209116bd9f"
 
 [[package]]
 name = "safetensors"
@@ -3177,11 +3564,11 @@ checksum = "94143f37725109f92c262ed2cf5e59bce7498c01bcc1502d7b9afe439a4e9f49"
 
 [[package]]
 name = "security-framework"
-version = "2.10.0"
+version = "2.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "770452e37cad93e0a50d5abc3990d2bc351c36d0328f86cefec2f2fb206eaef6"
+checksum = "c627723fd09706bacdb5cf41499e95098555af3c3c29d014dc3c458ef6be11c0"
 dependencies = [
- "bitflags 1.3.2",
+ "bitflags 2.6.0",
  "core-foundation",
  "core-foundation-sys",
  "libc",
@@ -3190,9 +3577,9 @@ dependencies = [
 
 [[package]]
 name = "security-framework-sys"
-version = "2.10.0"
+version = "2.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "41f3cc463c0ef97e11c3461a9d3787412d30e8e7eb907c79180c4a57bf7c04ef"
+checksum = "317936bbbd05227752583946b9e66d7ce3b489f84e11a94a510b4437fef407d7"
 dependencies = [
  "core-foundation-sys",
  "libc",
@@ -3206,29 +3593,29 @@ checksum = "a3f0bf26fd526d2a95683cd0f87bf103b8539e2ca1ef48ce002d67aad59aa0b4"
 
 [[package]]
 name = "serde"
-version = "1.0.197"
+version = "1.0.203"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3fb1c873e1b9b056a4dc4c0c198b24c3ffa059243875552b2bd0933b1aee4ce2"
+checksum = "7253ab4de971e72fb7be983802300c30b5a7f0c2e56fab8abfc6a214307c0094"
 dependencies = [
  "serde_derive",
 ]
 
 [[package]]
 name = "serde_derive"
-version = "1.0.197"
+version = "1.0.203"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7eb0b34b42edc17f6b7cac84a52a1c5f0e1bb2227e997ca9011ea3dd34e8610b"
+checksum = "500cbc0ebeb6f46627f50f3f5811ccf6bf00643be300b4c3eabc0ef55dc5b5ba"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
 name = "serde_json"
-version = "1.0.116"
+version = "1.0.120"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3e17db7126d17feb94eb3fad46bf1a96b034e8aacbc2e775fe81505f8b0b2813"
+checksum = "4e0d21c9a8cae1235ad58a00c11cb40d4b1e5c784f1ef2c537876ed6ffd8b7c5"
 dependencies = [
  "itoa",
  "ryu",
@@ -3256,9 +3643,9 @@ dependencies = [
 
 [[package]]
 name = "serde_spanned"
-version = "0.6.5"
+version = "0.6.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "eb3622f419d1296904700073ea6cc23ad690adbd66f13ea683df73298736f0c1"
+checksum = "79e674e01f999af37c49f70a6ede167a8a60b2503e56c5599532a65baa5969a0"
 dependencies = [
  "serde",
 ]
@@ -3310,7 +3697,7 @@ checksum = "91d129178576168c589c9ec973feedf7d3126c01ac2bf08795109aa35b69fb8f"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -3333,11 +3720,17 @@ dependencies = [
  "lazy_static",
 ]
 
+[[package]]
+name = "shlex"
+version = "1.3.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0fda2ff0d084019ba4d7c6f371c95d8fd75ce3524c3cb8fb653a3023f6323e64"
+
 [[package]]
 name = "signal-hook-registry"
-version = "1.4.1"
+version = "1.4.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d8229b473baa5980ac72ef434c4415e70c4b5e71b423043adb4ba059f89c99a1"
+checksum = "a9e9e0b4211b72e7b8b6e85c807d36c212bdb33ea8587f7569562a84df5465b1"
 dependencies = [
  "libc",
 ]
@@ -3371,25 +3764,14 @@ checksum = "3c5e1a9a646d36c3599cd173a41282daf47c44583ad367b8e6837255952e5c67"
 
 [[package]]
 name = "socket2"
-version = "0.5.6"
+version = "0.5.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "05ffd9c0a93b7543e062e759284fcf5f5e3b098501104bfbdde4d404db792871"
+checksum = "ce305eb0b4296696835b71df73eb912e0f1ffd2556a501fcede6e0c50349191c"
 dependencies = [
  "libc",
  "windows-sys 0.52.0",
 ]
 
-[[package]]
-name = "socks"
-version = "0.3.4"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f0c3dbbd9ae980613c6dd8e28a9407b50509d3803b57624d5dfe8315218cd58b"
-dependencies = [
- "byteorder",
- "libc",
- "winapi",
-]
-
 [[package]]
 name = "spin"
 version = "0.9.8"
@@ -3416,21 +3798,34 @@ checksum = "a8f112729512f8e442d81f95a8a7ddf2b7c6b8a1a6f509a95864142b30cab2d3"
 
 [[package]]
 name = "strsim"
-version = "0.10.0"
+version = "0.11.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "73473c0e59e6d5812c5dfe2a064a6444949f089e20eec9a2e5506596494e4623"
+checksum = "7da8b5736845d9f2fcb837ea5d9e2628564b3b043a70948a3f0b778838c5fb4f"
 
 [[package]]
-name = "strsim"
-version = "0.11.1"
+name = "strum"
+version = "0.26.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7da8b5736845d9f2fcb837ea5d9e2628564b3b043a70948a3f0b778838c5fb4f"
+checksum = "8fec0f0aef304996cf250b31b5a10dee7980c85da9d759361292b8bca5a18f06"
+
+[[package]]
+name = "strum_macros"
+version = "0.26.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4c6bee85a5a24955dc440386795aa378cd9cf82acd5f764469152d2270e581be"
+dependencies = [
+ "heck 0.5.0",
+ "proc-macro2",
+ "quote",
+ "rustversion",
+ "syn 2.0.68",
+]
 
 [[package]]
 name = "subtle"
-version = "2.5.0"
+version = "2.6.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "81cdd64d312baedb58e21336b31bc043b77e01cc99033ce76ef539f78e965ebc"
+checksum = "13c2bddecc57b384dee18652358fb23172facb8a2c51ccc10d74c157bdea3292"
 
 [[package]]
 name = "syn"
@@ -3445,9 +3840,9 @@ dependencies = [
 
 [[package]]
 name = "syn"
-version = "2.0.59"
+version = "2.0.68"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4a6531ffc7b071655e4ce2e04bd464c4830bb585a61cabb96cf808f05172615a"
+checksum = "901fa70d88b9d6c98022e23b4136f9f3e54e4662c3bc1bd1d84a42a9a0f0c1e9"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -3474,7 +3869,7 @@ checksum = "c8af7666ab7b6390ab78131fb5b0fce11d6b7a6951602017c35fa82800708971"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -3483,7 +3878,7 @@ version = "0.5.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "ec7dddc5f0fee506baf8b9fdb989e242f17e4b11c61dfbb0635b705217199eea"
 dependencies = [
- "bitflags 2.5.0",
+ "bitflags 2.6.0",
  "byteorder",
  "enum-as-inner",
  "libc",
@@ -3514,9 +3909,9 @@ dependencies = [
 
 [[package]]
 name = "tar"
-version = "0.4.40"
+version = "0.4.41"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b16afcea1f22891c49a00c751c7b63b2233284064f11a200fc624137c51e2ddb"
+checksum = "cb797dad5fb5b76fcf519e702f4a589483b5ef06567f160c392832c1f5e44909"
 dependencies = [
  "filetime",
  "libc",
@@ -3537,7 +3932,7 @@ dependencies = [
 
 [[package]]
 name = "text-embeddings-backend"
-version = "1.2.2"
+version = "1.3.0"
 dependencies = [
  "clap",
  "rand",
@@ -3550,7 +3945,7 @@ dependencies = [
 
 [[package]]
 name = "text-embeddings-backend-candle"
-version = "1.2.2"
+version = "1.3.0"
 dependencies = [
  "accelerate-src",
  "anyhow",
@@ -3580,7 +3975,7 @@ dependencies = [
 
 [[package]]
 name = "text-embeddings-backend-core"
-version = "1.2.2"
+version = "1.3.0"
 dependencies = [
  "clap",
  "nohash-hasher",
@@ -3589,7 +3984,7 @@ dependencies = [
 
 [[package]]
 name = "text-embeddings-backend-python"
-version = "1.2.2"
+version = "1.3.0"
 dependencies = [
  "backend-grpc-client",
  "nohash-hasher",
@@ -3603,10 +3998,12 @@ dependencies = [
 
 [[package]]
 name = "text-embeddings-core"
-version = "1.2.2"
+version = "1.3.0"
 dependencies = [
+ "async-channel",
  "hf-hub",
  "metrics",
+ "serde_json",
  "text-embeddings-backend",
  "thiserror",
  "tokenizers",
@@ -3616,12 +4013,13 @@ dependencies = [
 
 [[package]]
 name = "text-embeddings-router"
-version = "1.2.2"
+version = "1.3.0"
 dependencies = [
  "anyhow",
  "async-stream",
  "axum 0.7.5",
  "axum-tracing-opentelemetry",
+ "base64 0.22.1",
  "clap",
  "futures",
  "hf-hub",
@@ -3629,14 +4027,16 @@ dependencies = [
  "init-tracing-opentelemetry",
  "insta",
  "is_close",
+ "libc",
  "metrics",
  "metrics-exporter-prometheus",
  "mimalloc",
  "num_cpus",
- "opentelemetry 0.20.0",
- "opentelemetry-otlp",
- "prost 0.12.4",
- "reqwest",
+ "opentelemetry 0.23.0",
+ "opentelemetry-otlp 0.16.0",
+ "opentelemetry_sdk 0.23.0",
+ "prost 0.12.6",
+ "reqwest 0.12.5",
  "serde",
  "serde_json",
  "text-embeddings-backend",
@@ -3646,12 +4046,12 @@ dependencies = [
  "tokio",
  "tokio-stream",
  "tonic 0.11.0",
- "tonic-build 0.10.2",
+ "tonic-build 0.11.0",
  "tonic-health",
  "tonic-reflection",
  "tower-http",
  "tracing",
- "tracing-opentelemetry 0.21.0",
+ "tracing-opentelemetry 0.24.0",
  "tracing-subscriber",
  "utoipa",
  "utoipa-swagger-ui",
@@ -3661,22 +4061,22 @@ dependencies = [
 
 [[package]]
 name = "thiserror"
-version = "1.0.58"
+version = "1.0.61"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "03468839009160513471e86a034bb2c5c0e4baae3b43f79ffc55c4a5427b3297"
+checksum = "c546c80d6be4bc6a00c0f01730c08df82eaa7a7a61f11d656526506112cc1709"
 dependencies = [
  "thiserror-impl",
 ]
 
 [[package]]
 name = "thiserror-impl"
-version = "1.0.58"
+version = "1.0.61"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c61f3ba182994efc43764a46c018c347bc492c79f024e705f46567b418f6d4f7"
+checksum = "46c3384250002a6d5af4d114f2845d37b57521033f30d5c3f46c4d70e1197533"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -3724,9 +4124,9 @@ dependencies = [
 
 [[package]]
 name = "tinyvec"
-version = "1.6.0"
+version = "1.6.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "87cc5ceb3875bb20c2890005a4e226a4651264a5c75edb2421b52861a0a0cb50"
+checksum = "c55115c6fbe2d2bef26eb09ad74bde02d8255476fc0c7b515ef09fbb35742d82"
 dependencies = [
  "tinyvec_macros",
 ]
@@ -3739,12 +4139,12 @@ checksum = "1f3ccbac311fea05f86f61904b462b55fb3df8837a366dfc601a0161d0532f20"
 
 [[package]]
 name = "tokenizers"
-version = "0.15.2"
+version = "0.19.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3dd47962b0ba36e7fd33518fbf1754d136fd1474000162bbf2a8b5fcb2d3654d"
+checksum = "e500fad1dd3af3d626327e6a3fe5050e664a6eaa4708b8ca92f1794aaf73e6fd"
 dependencies = [
  "aho-corasick",
- "derive_builder 0.12.0",
+ "derive_builder",
  "esaxx-rs",
  "getrandom",
  "itertools 0.12.1",
@@ -3758,7 +4158,7 @@ dependencies = [
  "rayon",
  "rayon-cond",
  "regex",
- "regex-syntax 0.8.3",
+ "regex-syntax 0.8.4",
  "serde",
  "serde_json",
  "spm_precompiled",
@@ -3770,9 +4170,9 @@ dependencies = [
 
 [[package]]
 name = "tokio"
-version = "1.37.0"
+version = "1.38.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1adbebffeca75fcfd058afa480fb6c0b81e165a0323f9c9d39c9697e37c46787"
+checksum = "ba4f4a02a7a80d6f274636f0aa95c7e383b912d41fe721a31f29e29698585a4a"
 dependencies = [
  "backtrace",
  "bytes",
@@ -3799,13 +4199,13 @@ dependencies = [
 
 [[package]]
 name = "tokio-macros"
-version = "2.2.0"
+version = "2.3.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5b8a1e28f2deaa14e508979454cb3a223b10b938b45af148bc0986de36f1923b"
+checksum = "5f5ae998a069d4b5aba8ee9dad856af7d520c3699e6159b185c2acd48155d39a"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -3818,6 +4218,17 @@ dependencies = [
  "tokio",
 ]
 
+[[package]]
+name = "tokio-rustls"
+version = "0.26.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0c7bc40d0e5a97695bb96e27995cd3a08538541b0a846f65bba7a359f36700d4"
+dependencies = [
+ "rustls 0.23.10",
+ "rustls-pki-types",
+ "tokio",
+]
+
 [[package]]
 name = "tokio-stream"
 version = "0.1.15"
@@ -3831,50 +4242,60 @@ dependencies = [
 
 [[package]]
 name = "tokio-util"
-version = "0.7.10"
+version = "0.7.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5419f34732d9eb6ee4c3578b7989078579b7f039cbbb9ca2c4da015749371e15"
+checksum = "9cf6b47b3771c49ac75ad09a6162f53ad4b8088b76ac60e8ec1455b31a189fe1"
 dependencies = [
  "bytes",
  "futures-core",
  "futures-sink",
  "pin-project-lite",
  "tokio",
- "tracing",
 ]
 
 [[package]]
 name = "toml"
-version = "0.8.12"
+version = "0.8.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e9dd1545e8208b4a5af1aa9bbd0b4cf7e9ea08fabc5d0a5c67fcaafa17433aa3"
+checksum = "6f49eb2ab21d2f26bd6db7bf383edc527a7ebaee412d17af4d40fdccd442f335"
 dependencies = [
  "serde",
  "serde_spanned",
  "toml_datetime",
- "toml_edit",
+ "toml_edit 0.22.14",
 ]
 
 [[package]]
 name = "toml_datetime"
-version = "0.6.5"
+version = "0.6.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3550f4e9685620ac18a50ed434eb3aec30db8ba93b0287467bca5826ea25baf1"
+checksum = "4badfd56924ae69bcc9039335b2e017639ce3f9b001c393c1b2d1ef846ce2cbf"
 dependencies = [
  "serde",
 ]
 
 [[package]]
 name = "toml_edit"
-version = "0.22.9"
+version = "0.21.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6a8534fd7f78b5405e860340ad6575217ce99f38d4d5c8f2442cb5ecb50090e1"
+dependencies = [
+ "indexmap 2.2.6",
+ "toml_datetime",
+ "winnow 0.5.40",
+]
+
+[[package]]
+name = "toml_edit"
+version = "0.22.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8e40bb779c5187258fd7aad0eb68cb8706a0a81fa712fbea808ab43c4b8374c4"
+checksum = "f21c7aaf97f1bd9ca9d4f9e73b0a6c74bd5afef56f2bc931943a6e1c37e04e38"
 dependencies = [
  "indexmap 2.2.6",
  "serde",
  "serde_spanned",
  "toml_datetime",
- "winnow",
+ "winnow 0.6.13",
 ]
 
 [[package]]
@@ -3889,10 +4310,10 @@ dependencies = [
  "bytes",
  "futures-core",
  "futures-util",
- "h2",
+ "h2 0.3.26",
  "http 0.2.12",
  "http-body 0.4.6",
- "hyper 0.14.28",
+ "hyper 0.14.29",
  "hyper-timeout",
  "percent-encoding",
  "pin-project",
@@ -3916,14 +4337,14 @@ dependencies = [
  "axum 0.6.20",
  "base64 0.21.7",
  "bytes",
- "h2",
+ "h2 0.3.26",
  "http 0.2.12",
  "http-body 0.4.6",
- "hyper 0.14.28",
+ "hyper 0.14.29",
  "hyper-timeout",
  "percent-encoding",
  "pin-project",
- "prost 0.12.4",
+ "prost 0.12.6",
  "tokio",
  "tokio-stream",
  "tower",
@@ -3947,15 +4368,15 @@ dependencies = [
 
 [[package]]
 name = "tonic-build"
-version = "0.10.2"
+version = "0.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9d021fc044c18582b9a2408cd0dd05b1596e3ecdb5c4df822bb0183545683889"
+checksum = "be4ef6dd70a610078cb4e338a0f79d06bc759ff1b22d2120c2ff02ae264ba9c2"
 dependencies = [
- "prettyplease 0.2.19",
+ "prettyplease 0.2.20",
  "proc-macro2",
- "prost-build 0.12.4",
+ "prost-build 0.12.6",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -3965,7 +4386,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "2cef6e24bc96871001a7e48e820ab240b3de2201e59b517cf52835df2f1d2350"
 dependencies = [
  "async-stream",
- "prost 0.12.4",
+ "prost 0.12.6",
  "tokio",
  "tokio-stream",
  "tonic 0.11.0",
@@ -3977,8 +4398,8 @@ version = "0.11.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "548c227bd5c0fae5925812c4ec6c66ffcfced23ea370cb823f4d18f0fc1cb6a7"
 dependencies = [
- "prost 0.12.4",
- "prost-types 0.12.4",
+ "prost 0.12.6",
+ "prost-types 0.12.6",
  "tokio",
  "tokio-stream",
  "tonic 0.11.0",
@@ -4010,7 +4431,7 @@ version = "0.5.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "1e9cd434a998747dd2c4276bc96ee2e0c7a2eadf3cae88e52be55a05fa9053f5"
 dependencies = [
- "bitflags 2.5.0",
+ "bitflags 2.6.0",
  "bytes",
  "http 1.1.0",
  "http-body 1.0.0",
@@ -4052,7 +4473,7 @@ checksum = "34704c8d6ebcbc939824180af020566b01a7c01f80641264eba0999f6c2b6be7"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -4103,30 +4524,32 @@ dependencies = [
 
 [[package]]
 name = "tracing-opentelemetry"
-version = "0.21.0"
+version = "0.23.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "75327c6b667828ddc28f5e3f169036cb793c3f588d83bf0f262a7f062ffed3c8"
+checksum = "a9be14ba1bbe4ab79e9229f7f89fab8d120b865859f10527f31c033e599d2284"
 dependencies = [
+ "js-sys",
  "once_cell",
- "opentelemetry 0.20.0",
- "opentelemetry_sdk 0.20.0",
+ "opentelemetry 0.22.0",
+ "opentelemetry_sdk 0.22.1",
  "smallvec",
  "tracing",
  "tracing-core",
- "tracing-log 0.1.4",
+ "tracing-log 0.2.0",
  "tracing-subscriber",
+ "web-time",
 ]
 
 [[package]]
 name = "tracing-opentelemetry"
-version = "0.22.0"
+version = "0.24.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c67ac25c5407e7b961fafc6f7e9aa5958fd297aada2d20fa2ae1737357e55596"
+checksum = "f68803492bf28ab40aeccaecc7021096bd256baf7ca77c3d425d89b35a7be4e4"
 dependencies = [
  "js-sys",
  "once_cell",
- "opentelemetry 0.21.0",
- "opentelemetry_sdk 0.21.2",
+ "opentelemetry 0.23.0",
+ "opentelemetry_sdk 0.23.0",
  "smallvec",
  "tracing",
  "tracing-core",
@@ -4137,14 +4560,14 @@ dependencies = [
 
 [[package]]
 name = "tracing-opentelemetry-instrumentation-sdk"
-version = "0.17.1"
+version = "0.18.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9db99a4f5224920c499515a737e2749eb9a19b729b3880afc24594524e9861de"
+checksum = "9a276058193f1b03d8279356215ec4c8c1bb21e40e5554bb239aa94fb2d8e189"
 dependencies = [
  "http 1.1.0",
- "opentelemetry 0.21.0",
+ "opentelemetry 0.22.0",
  "tracing",
- "tracing-opentelemetry 0.22.0",
+ "tracing-opentelemetry 0.23.0",
 ]
 
 [[package]]
@@ -4237,9 +4660,9 @@ checksum = "d4c87d22b6e3f4a18d4d40ef354e97c90fcb14dd91d7dc0aa9d8a1172ebf7202"
 
 [[package]]
 name = "unicode-width"
-version = "0.1.11"
+version = "0.1.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e51733f11c9c4f72aa0c160008246859e340b00807569a0da0e7a1079b27ba85"
+checksum = "0336d538f7abc86d282a4189614dfaa90810dfc2c6f6427eaf88e16311dd225d"
 
 [[package]]
 name = "unicode_categories"
@@ -4261,30 +4684,29 @@ checksum = "8ecb6da28b8a351d773b68d5825ac39017e680750f980f3a1a85cd8dd28a47c1"
 
 [[package]]
 name = "ureq"
-version = "2.9.6"
+version = "2.9.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "11f214ce18d8b2cbe84ed3aa6486ed3f5b285cf8d8fbdbce9f3f767a724adc35"
+checksum = "d11a831e3c0b56e438a28308e7c810799e3c118417f342d30ecec080105395cd"
 dependencies = [
- "base64 0.21.7",
+ "base64 0.22.1",
  "flate2",
  "log",
  "native-tls",
  "once_cell",
- "rustls",
+ "rustls 0.22.4",
  "rustls-pki-types",
  "rustls-webpki",
  "serde",
  "serde_json",
- "socks",
  "url",
  "webpki-roots",
 ]
 
 [[package]]
 name = "url"
-version = "2.5.0"
+version = "2.5.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "31e6302e3bb753d46e83516cae55ae196fc0c309407cf11ab35cc51a4c2a4633"
+checksum = "22784dbdf76fdde8af1aeda5622b546b422b6fc585325248a2bf9f5e41e94d6c"
 dependencies = [
  "form_urlencoded",
  "idna",
@@ -4299,15 +4721,15 @@ checksum = "daf8dba3b7eb870caf1ddeed7bc9d2a049f3cfdfae7cb521b087cc33ae4c49da"
 
 [[package]]
 name = "utf8parse"
-version = "0.2.1"
+version = "0.2.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "711b9620af191e0cdc7468a8d14e709c3dcdb115b36f838e601583af800a370a"
+checksum = "06abde3611657adf66d383f00b093d7faecc7fa57071cce2578660c9f1010821"
 
 [[package]]
 name = "utoipa"
-version = "4.2.0"
+version = "4.2.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "272ebdfbc99111033031d2f10e018836056e4d2c8e2acda76450ec7974269fa7"
+checksum = "c5afb1a60e207dca502682537fefcfd9921e71d0b83e9576060f09abc6efab23"
 dependencies = [
  "indexmap 2.2.6",
  "serde",
@@ -4317,38 +4739,40 @@ dependencies = [
 
 [[package]]
 name = "utoipa-gen"
-version = "4.2.0"
+version = "4.3.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d3c9f4d08338c1bfa70dde39412a040a884c6f318b3d09aaaf3437a1e52027fc"
+checksum = "7bf0e16c02bc4bf5322ab65f10ab1149bdbcaa782cba66dc7057370a3f8190be"
 dependencies = [
  "proc-macro-error",
  "proc-macro2",
  "quote",
  "regex",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
 name = "utoipa-swagger-ui"
-version = "6.0.0"
+version = "7.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0b39868d43c011961e04b41623e050aedf2cc93652562ff7935ce0f819aaf2da"
+checksum = "943e0ff606c6d57d410fd5663a4d7c074ab2c5f14ab903b9514565e59fa1189e"
 dependencies = [
  "axum 0.7.5",
  "mime_guess",
  "regex",
+ "reqwest 0.12.5",
  "rust-embed",
  "serde",
  "serde_json",
+ "url",
  "utoipa",
- "zip",
+ "zip 1.1.4",
 ]
 
 [[package]]
 name = "uuid"
-version = "1.8.0"
+version = "1.9.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a183cf7feeba97b4dd1c0d46788634f6221d87fa961b305bed08c851829efcc0"
+checksum = "5de17fd2f7da591098415cff336e12965a28061ddace43b59cb3c430179c9439"
 dependencies = [
  "getrandom",
 ]
@@ -4383,7 +4807,7 @@ checksum = "cff2381c6b31ab2555441e382d699a56c3551d0cfdf0c4df5617bf271c1dd102"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
@@ -4450,7 +4874,7 @@ dependencies = [
  "once_cell",
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
  "wasm-bindgen-shared",
 ]
 
@@ -4484,7 +4908,7 @@ checksum = "e94f17b526d0a461a191c78ea52bbce64071ed5c04c9ffe424dcb38f74171bb7"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
  "wasm-bindgen-backend",
  "wasm-bindgen-shared",
 ]
@@ -4507,9 +4931,9 @@ dependencies = [
 
 [[package]]
 name = "web-time"
-version = "0.2.4"
+version = "1.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "aa30049b1c872b72c89866d458eae9f20380ab280ffd1b1e18df2d3e2d98cfe0"
+checksum = "5a6580f308b1fad9207618087a65c04e7a10bc77e02c8e84e9b00dd4b12fa0bb"
 dependencies = [
  "js-sys",
  "wasm-bindgen",
@@ -4517,9 +4941,9 @@ dependencies = [
 
 [[package]]
 name = "webpki-roots"
-version = "0.26.1"
+version = "0.26.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b3de34ae270483955a94f4b21bdaaeb83d508bb84a01435f393818edb0012009"
+checksum = "bd7c23921eeb1713a4e851530e9b9756e4fb0e89978582942612524cf09f01cd"
 dependencies = [
  "rustls-pki-types",
 ]
@@ -4554,11 +4978,11 @@ checksum = "ac3b87c63620426dd9b991e5ce0329eff545bccbbb34f3be09ff6fb6ab51b7b6"
 
 [[package]]
 name = "winapi-util"
-version = "0.1.6"
+version = "0.1.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f29e6f9198ba0d26b4c9f07dbe6f9ed633e1f3d5b8b414090084349e46a52596"
+checksum = "4d4cc384e1e73b93bafa6fb4f1df8c41695c8a91cf9c4c64358067d15a7b6c6b"
 dependencies = [
- "winapi",
+ "windows-sys 0.52.0",
 ]
 
 [[package]]
@@ -4717,9 +5141,18 @@ checksum = "bec47e5bfd1bff0eeaf6d8b485cc1074891a197ab4225d504cb7a1ab88b02bf0"
 
 [[package]]
 name = "winnow"
-version = "0.6.6"
+version = "0.5.40"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f593a95398737aeed53e489c785df13f3618e41dbcd6718c6addbf1395aa6876"
+dependencies = [
+ "memchr",
+]
+
+[[package]]
+name = "winnow"
+version = "0.6.13"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f0c976aaaa0e1f90dbb21e9587cdaf1d9679a1cde8875c0d6bd83ab96a208352"
+checksum = "59b5e5f6c299a3c7890b876a2a587f3115162487e704907d9b6cd29473052ba1"
 dependencies = [
  "memchr",
 ]
@@ -4734,6 +5167,16 @@ dependencies = [
  "windows-sys 0.48.0",
 ]
 
+[[package]]
+name = "winreg"
+version = "0.52.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a277a57398d4bfa075df44f501a17cfdf8542d224f0d36095a2adc7aee4ef0a5"
+dependencies = [
+ "cfg-if",
+ "windows-sys 0.48.0",
+]
+
 [[package]]
 name = "xattr"
 version = "1.3.1"
@@ -4756,9 +5199,9 @@ dependencies = [
 
 [[package]]
 name = "yoke"
-version = "0.7.3"
+version = "0.7.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "65e71b2e4f287f467794c671e2b8f8a5f3716b3c829079a1c44740148eff07e4"
+checksum = "6c5b1314b079b0930c31e3af543d8ee1757b1951ae1e1565ec704403a7240ca5"
 dependencies = [
  "serde",
  "stable_deref_trait",
@@ -4768,62 +5211,76 @@ dependencies = [
 
 [[package]]
 name = "yoke-derive"
-version = "0.7.3"
+version = "0.7.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9e6936f0cce458098a201c245a11bef556c6a0181129c7034d10d76d1ec3a2b8"
+checksum = "28cc31741b18cb6f1d5ff12f5b7523e3d6eb0852bbbad19d73905511d9849b95"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
  "synstructure",
 ]
 
 [[package]]
 name = "zerocopy"
-version = "0.7.32"
+version = "0.7.35"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "74d4d3961e53fa4c9a25a8637fc2bfaf2595b3d3ae34875568a5cf64787716be"
+checksum = "1b9b4fd18abc82b8136838da5d50bae7bdea537c574d8dc1a34ed098d6c166f0"
 dependencies = [
  "zerocopy-derive",
 ]
 
 [[package]]
 name = "zerocopy-derive"
-version = "0.7.32"
+version = "0.7.35"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9ce1b18ccd8e73a9321186f97e46f9f04b778851177567b1975109d26a08d2a6"
+checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
 ]
 
 [[package]]
 name = "zerofrom"
-version = "0.1.3"
+version = "0.1.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "655b0814c5c0b19ade497851070c640773304939a6c0fd5f5fb43da0696d05b7"
+checksum = "91ec111ce797d0e0784a1116d0ddcdbea84322cd79e5d5ad173daeba4f93ab55"
 dependencies = [
  "zerofrom-derive",
 ]
 
 [[package]]
 name = "zerofrom-derive"
-version = "0.1.3"
+version = "0.1.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e6a647510471d372f2e6c2e6b7219e44d8c574d24fdc11c610a61455782f18c3"
+checksum = "0ea7b4a3637ea8669cedf0f1fd5c286a17f3de97b8dd5a70a6c167a1730e63a5"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.59",
+ "syn 2.0.68",
  "synstructure",
 ]
 
 [[package]]
 name = "zeroize"
-version = "1.7.0"
+version = "1.8.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ced3678a2879b30306d323f4542626697a464a97c0a07c9aebf7ebca65cd4dde"
+dependencies = [
+ "zeroize_derive",
+]
+
+[[package]]
+name = "zeroize_derive"
+version = "1.4.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "525b4ec142c6b68a2d10f01f7bbf6755599ca3f81ea53b8431b7dd348f5fdb2d"
+checksum = "ce36e65b0d2999d2aafac989fb249189a141aee1f53c612c1f37d72631959f69"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.68",
+]
 
 [[package]]
 name = "zip"
@@ -4834,5 +5291,20 @@ dependencies = [
  "byteorder",
  "crc32fast",
  "crossbeam-utils",
+]
+
+[[package]]
+name = "zip"
+version = "1.1.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9cc23c04387f4da0374be4533ad1208cbb091d5c11d070dfef13676ad6497164"
+dependencies = [
+ "arbitrary",
+ "crc32fast",
+ "crossbeam-utils",
+ "displaydoc",
  "flate2",
+ "indexmap 2.2.6",
+ "num_enum",
+ "thiserror",
 ]
diff --git a/Cargo.toml b/Cargo.toml
index f392fb9b..9e1c26cd 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -11,25 +11,43 @@ members = [
 resolver = "2"
 
 [workspace.package]
-version = "1.2.2"
+version = "1.4.0"
 edition = "2021"
 authors = ["Olivier Dehaene"]
 homepage = "https://github.com/huggingface/text-embeddings-inference"
 
+[workspace.dependencies]
+anyhow = "1.0.75"
+clap = { version = "4.1", features = ["derive", "env"] }
+hf-hub = { version = "0.3.2", features = ["tokio", "online"], default-features = false }
+metrics = "0.23"
+nohash-hasher = "0.2"
+tokenizers = { version = "0.19.1", default-features = false, features = ["onig", "esaxx_fast"] }
+tokio = { version = "1.25", features = ["rt", "rt-multi-thread", "parking_lot", "sync", "signal"] }
+tracing = "0.1"
+serde = { version = "1.0", features = ["serde_derive"] }
+serde_json = "1.0"
+thiserror = "1.0"
+
+
 [patch.crates-io]
 cudarc = { git = "https://github.com/coreylowman/cudarc", rev = "c388e724af93a3e8fbe484f5ded2d8b3c1badd8e" }
 candle = { git = "https://github.com/OlivierDehaene/candle", rev = "33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3", package = "candle-core" }
 candle-nn = { git = "https://github.com/OlivierDehaene/candle", rev = "33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3", package = "candle-nn" }
 candle-transformers = { git = "https://github.com/OlivierDehaene/candle", rev = "33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3", package = "candle-transformers" }
 candle-flash-attn = { git = "https://github.com/OlivierDehaene/candle", rev = "33b7ecf9ed82bb7c20f1a94555218fabfbaa2fe3", package = "candle-flash-attn" }
-hf-hub = { git = "https://github.com/huggingface/hf-hub", rev = "b167f69692be5f49eb8003788f7f8a499a98b096" }
-
 
 [profile.release]
 debug = 0
-incremental = true
 lto = "fat"
 opt-level = 3
 codegen-units = 1
 strip = "symbols"
 panic = "abort"
+
+[profile.release-debug]
+inherits = "release"
+debug = 1
+lto = "thin"
+codegen-units = 16
+strip = "none"
diff --git a/Dockerfile b/Dockerfile
index 625d165f..8c1368c0 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -4,7 +4,7 @@ WORKDIR /usr/src
 ENV SCCACHE=0.5.4
 ENV RUSTC_WRAPPER=/usr/local/bin/sccache
 
-# Donwload and configure sccache
+# Donwload, configure sccache
 RUN curl -fsSL https://github.com/mozilla/sccache/releases/download/v$SCCACHE/sccache-v$SCCACHE-x86_64-unknown-linux-musl.tar.gz | tar -xzv --strip-components=1 -C /usr/local/bin sccache-v$SCCACHE-x86_64-unknown-linux-musl/sccache && \
     chmod +x /usr/local/bin/sccache
 
diff --git a/Dockerfile-cuda b/Dockerfile-cuda
index 990e4261..d67eba93 100644
--- a/Dockerfile-cuda
+++ b/Dockerfile-cuda
@@ -35,6 +35,11 @@ ARG CUDA_COMPUTE_CAP=80
 ARG GIT_SHA
 ARG DOCKER_LABEL
 
+# Limit parallelism
+ARG RAYON_NUM_THREADS
+ARG CARGO_BUILD_JOBS
+ARG CARGO_BUILD_INCREMENTAL
+
 # sccache specific variables
 ARG ACTIONS_CACHE_URL
 ARG ACTIONS_RUNTIME_TOKEN
diff --git a/Dockerfile-cuda-all b/Dockerfile-cuda-all
index aa9fe1f9..43150277 100644
--- a/Dockerfile-cuda-all
+++ b/Dockerfile-cuda-all
@@ -33,49 +33,48 @@ FROM base-builder AS builder
 
 ARG GIT_SHA
 ARG DOCKER_LABEL
-ARG VERTEX
+ARG VERTEX="false"
 
 # sccache specific variables
 ARG ACTIONS_CACHE_URL
 ARG ACTIONS_RUNTIME_TOKEN
 ARG SCCACHE_GHA_ENABLED
 
-# limit the number of kernels built at the same time
-ARG RAYON_NUM_THREADS=2
+# Limit parallelism
+ARG RAYON_NUM_THREADS=4
+ARG CARGO_BUILD_JOBS
+ARG CARGO_BUILD_INCREMENTAL
 
 WORKDIR /usr/src
 
 COPY --from=planner /usr/src/recipe.json recipe.json
 
-FROM builder as builder-75
-
 RUN if [ $VERTEX = "true" ]; \
     then \
-      CUDA_COMPUTE_CAP=75 cargo chef cook --release --features google --features candle-cuda-turing --features http --no-default-features --recipe-path recipe.json && sccache -s; \
+      cargo chef cook --release --features google --recipe-path recipe.json && sccache -s; \
     else \
-      CUDA_COMPUTE_CAP=75 cargo chef cook --release --features candle-cuda-turing --no-default-features --features http --recipe-path recipe.json && sccache -s; \
+      cargo chef cook --release --recipe-path recipe.json && sccache -s; \
     fi;
 
-COPY backends backends
-COPY core core
-COPY router router
-COPY Cargo.toml ./
-COPY Cargo.lock ./
-
 RUN if [ $VERTEX = "true" ]; \
     then \
-        CUDA_COMPUTE_CAP=75 cargo build --release --bin text-embeddings-router -F candle-cuda-turing -F http -F google --no-default-features && sccache -s; \
+      CUDA_COMPUTE_CAP=75 cargo chef cook --release --features google --features candle-cuda-turing --recipe-path recipe.json && sccache -s; \
     else \
-        CUDA_COMPUTE_CAP=75 cargo build --release --bin text-embeddings-router -F candle-cuda-turing -F http --no-default-features && sccache -s; \
+      CUDA_COMPUTE_CAP=75 cargo chef cook --release --features candle-cuda-turing --recipe-path recipe.json && sccache -s; \
     fi;
 
-FROM builder as builder-80
+RUN if [ $VERTEX = "true" ]; \
+    then \
+      CUDA_COMPUTE_CAP=80 cargo chef cook --release --features google --features candle-cuda --recipe-path recipe.json && sccache -s; \
+    else \
+      CUDA_COMPUTE_CAP=80 cargo chef cook --release --features candle-cuda --recipe-path recipe.json && sccache -s; \
+    fi;
 
 RUN if [ $VERTEX = "true" ]; \
     then \
-      CUDA_COMPUTE_CAP=80 cargo chef cook --release --features google --features candle-cuda --features http --no-default-features --recipe-path recipe.json && sccache -s; \
+      CUDA_COMPUTE_CAP=90 cargo chef cook --release --features google --features candle-cuda --recipe-path recipe.json && sccache -s; \
     else \
-      CUDA_COMPUTE_CAP=80 cargo chef cook --release --features candle-cuda --no-default-features --features http --recipe-path recipe.json && sccache -s; \
+      CUDA_COMPUTE_CAP=90 cargo chef cook --release --features candle-cuda --recipe-path recipe.json && sccache -s; \
     fi;
 
 COPY backends backends
@@ -86,33 +85,31 @@ COPY Cargo.lock ./
 
 RUN if [ $VERTEX = "true" ]; \
     then \
-        CUDA_COMPUTE_CAP=80 cargo build --release --bin text-embeddings-router -F candle-cuda -F http -F google --no-default-features && sccache -s; \
+        CUDA_COMPUTE_CAP=75 cargo build --release --bin text-embeddings-router -F candle-cuda-turing -F http -F google --no-default-features && sccache -s; \
     else \
-        CUDA_COMPUTE_CAP=80 cargo build --release --bin text-embeddings-router -F candle-cuda -F http --no-default-features && sccache -s; \
+        CUDA_COMPUTE_CAP=75 cargo build --release --bin text-embeddings-router -F candle-cuda-turing -F http --no-default-features && sccache -s; \
     fi;
 
-FROM builder as builder-90
+RUN mv /usr/src/target/release/text-embeddings-router /usr/src/target/release/text-embeddings-router-75
 
 RUN if [ $VERTEX = "true" ]; \
     then \
-      CUDA_COMPUTE_CAP=90 cargo chef cook --release --features google --features candle-cuda --features http --no-default-features --recipe-path recipe.json && sccache -s; \
+        CUDA_COMPUTE_CAP=80 cargo build --release --bin text-embeddings-router -F candle-cuda -F google  && sccache -s; \
     else \
-      CUDA_COMPUTE_CAP=90 cargo chef cook --release --features candle-cuda --features http --no-default-features --recipe-path recipe.json && sccache -s; \
+        CUDA_COMPUTE_CAP=80 cargo build --release --bin text-embeddings-router -F candle-cuda && sccache -s; \
     fi;
 
-COPY backends backends
-COPY core core
-COPY router router
-COPY Cargo.toml ./
-COPY Cargo.lock ./
+RUN mv /usr/src/target/release/text-embeddings-router /usr/src/target/release/text-embeddings-router-80
 
 RUN if [ $VERTEX = "true" ]; \
     then \
-        CUDA_COMPUTE_CAP=90 cargo build --release --bin text-embeddings-router -F candle-cuda -F http -F google --no-default-features && sccache -s; \
+        CUDA_COMPUTE_CAP=90 cargo build --release --bin text-embeddings-router -F candle-cuda -F google  && sccache -s; \
     else \
-        CUDA_COMPUTE_CAP=90 cargo build --release --bin text-embeddings-router -F candle-cuda -F http --no-default-features && sccache -s; \
+        CUDA_COMPUTE_CAP=90 cargo build --release --bin text-embeddings-router -F candle-cuda && sccache -s; \
     fi;
 
+RUN mv /usr/src/target/release/text-embeddings-router /usr/src/target/release/text-embeddings-router-90
+
 FROM nvidia/cuda:12.2.0-runtime-ubuntu22.04 as base
 
 ARG DEFAULT_USE_FLASH_ATTENTION=True
@@ -121,9 +118,9 @@ ENV HUGGINGFACE_HUB_CACHE=/data \
     PORT=80 \
     USE_FLASH_ATTENTION=$DEFAULT_USE_FLASH_ATTENTION
 
-COPY --from=builder-75 /usr/src/target/release/text-embeddings-router /usr/local/bin/text-embeddings-router-75
-COPY --from=builder-80 /usr/src/target/release/text-embeddings-router /usr/local/bin/text-embeddings-router-80
-COPY --from=builder-90 /usr/src/target/release/text-embeddings-router /usr/local/bin/text-embeddings-router-90
+COPY --from=builder /usr/src/target/release/text-embeddings-router-75 /usr/local/bin/text-embeddings-router-75
+COPY --from=builder /usr/src/target/release/text-embeddings-router-80 /usr/local/bin/text-embeddings-router-80
+COPY --from=builder /usr/src/target/release/text-embeddings-router-90 /usr/local/bin/text-embeddings-router-90
 
 # Amazon SageMaker compatible image
 FROM base AS sagemaker
diff --git a/Makefile b/Makefile
index 1af63144..99a5ff59 100644
--- a/Makefile
+++ b/Makefile
@@ -1,11 +1,11 @@
 integration-tests:
-	cargo test --release
+	cargo test
 
 cuda-integration-tests:
-	cargo test -F text-embeddings-backend-candle/cuda -F text-embeddings-backend-candle/flash-attn -F text-embeddings-router/candle-cuda --release
+	cargo test -F text-embeddings-backend-candle/cuda -F text-embeddings-backend-candle/flash-attn -F text-embeddings-router/candle-cuda --profile release-debug
 
 integration-tests-review:
-	cargo insta test --review --release
+	cargo insta test --review
 
 cuda-integration-tests-review:
-	cargo insta test --review --features "text-embeddings-backend-candle/cuda text-embeddings-backend-candle/flash-attn text-embeddings-router/candle-cuda" --release
+	cargo insta test --review --features "text-embeddings-backend-candle/cuda text-embeddings-backend-candle/flash-attn text-embeddings-router/candle-cuda" --profile release-debug
diff --git a/backends/Cargo.toml b/backends/Cargo.toml
index 09cef744..3efbc52d 100644
--- a/backends/Cargo.toml
+++ b/backends/Cargo.toml
@@ -6,13 +6,14 @@ authors.workspace = true
 homepage.workspace = true
 
 [dependencies]
-clap = { version = "4.1.4", features = ["derive"], optional = true }
+clap = { workspace = true, optional = true }
 text-embeddings-backend-core = { path = "core" }
 text-embeddings-backend-python = { path = "python", optional = true }
 text-embeddings-backend-candle = { path = "candle", optional = true }
 tokio = { version = "^1.25", features = ["sync"] }
 tracing = "^0.1"
 rand = "^0.8"
+
 [features]
 clap = ["dep:clap", "text-embeddings-backend-core/clap"]
 python = ["dep:text-embeddings-backend-python"]
diff --git a/backends/candle/Cargo.toml b/backends/candle/Cargo.toml
index f80998f4..f26d213a 100644
--- a/backends/candle/Cargo.toml
+++ b/backends/candle/Cargo.toml
@@ -6,6 +6,7 @@ authors.workspace = true
 homepage.workspace = true
 
 [dependencies]
+anyhow = { workspace = true }
 accelerate-src = { version = "0.3.2", optional = true }
 intel-mkl-src = { version = "0.8.1", optional = true  }
 candle = { version = "*", package = "candle-core", default-features = false }
@@ -16,21 +17,21 @@ candle-flash-attn-v1 = { git = "https://github.com/huggingface/candle-flash-attn
 candle-cublaslt = { git = "https://github.com/huggingface/candle-cublaslt", rev = "cf789b7dd6d4abb19b03b9556442f94f0588b4a0", optional = true }
 candle-layer-norm = { git = "https://github.com/huggingface/candle-layer-norm", rev = "94c2add7d94c2d63aebde77f7534614e04dbaea1", optional = true }
 candle-rotary = { git = "https://github.com/huggingface/candle-rotary", rev = "0a718a0856569a92f3112e64f10d07e4447822e8", optional = true }
-nohash-hasher = "^0.2"
+nohash-hasher = { workspace = true }
 text-embeddings-backend-core = { path = "../core" }
-tracing = "^0.1"
+tracing = { workspace = true }
 safetensors = "^0.4"
-thiserror = "^1.0"
-serde = { version = "^1.0", features = ["serde_derive"] }
-serde_json = "^1.0"
+thiserror = { workspace = true }
+serde = { workspace = true }
+serde_json = { workspace = true }
 memmap2 = "^0.9"
 
 [dev-dependencies]
 insta = { git = "https://github.com/OlivierDehaene/insta", rev = "f4f98c0410b91fb5a28b10df98e4422955be9c2c", features = ["yaml"] }
 is_close = "0.1.3"
 hf-hub = "0.3.2"
-anyhow = "1.0.75"
-tokenizers = { version = "^0.15.0", default-features = false, features = ["onig", "esaxx_fast"] }
+anyhow = { workspace = true }
+tokenizers = { workspace = true }
 serial_test = "2.0.0"
 
 [build-dependencies]
diff --git a/backends/candle/src/compute_cap.rs b/backends/candle/src/compute_cap.rs
index a1c391ee..ac79fcf1 100644
--- a/backends/candle/src/compute_cap.rs
+++ b/backends/candle/src/compute_cap.rs
@@ -1,41 +1,26 @@
+use anyhow::Context;
+use candle::cuda_backend::cudarc::driver;
 use candle::cuda_backend::cudarc::driver::sys::CUdevice_attribute::{
     CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR,
 };
 use candle::cuda_backend::cudarc::driver::CudaDevice;
-use std::sync::Once;
 
-static INIT: Once = Once::new();
-static mut RUNTIME_COMPUTE_CAP: usize = 0;
-static mut COMPILE_COMPUTE_CAP: usize = 0;
-
-fn init_compute_caps() {
-    unsafe {
-        INIT.call_once(|| {
-            let device = CudaDevice::new(0).expect("cuda is not available");
-            let major = device
-                .attribute(CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR)
-                .unwrap();
-            let minor = device
-                .attribute(CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR)
-                .unwrap();
-            RUNTIME_COMPUTE_CAP = (major * 10 + minor) as usize;
-            COMPILE_COMPUTE_CAP = env!("CUDA_COMPUTE_CAP").parse::<usize>().unwrap();
-        });
-    }
-}
-
-pub fn get_compile_compute_cap() -> usize {
-    unsafe {
-        init_compute_caps();
-        COMPILE_COMPUTE_CAP
-    }
+pub fn get_compile_compute_cap() -> Result<usize, anyhow::Error> {
+    env!("CUDA_COMPUTE_CAP")
+        .parse::<usize>()
+        .context("Could not retrieve compile time CUDA_COMPUTE_CAP")
 }
 
-pub fn get_runtime_compute_cap() -> usize {
-    unsafe {
-        init_compute_caps();
-        RUNTIME_COMPUTE_CAP
-    }
+pub fn get_runtime_compute_cap() -> Result<usize, anyhow::Error> {
+    driver::result::init().context("CUDA is not available")?;
+    let device = CudaDevice::new(0).context("CUDA is not available")?;
+    let major = device
+        .attribute(CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR)
+        .context("Could not retrieve device compute capability major")?;
+    let minor = device
+        .attribute(CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR)
+        .context("Could not retrieve device compute capability minor")?;
+    Ok((major * 10 + minor) as usize)
 }
 
 fn compute_cap_matching(runtime_compute_cap: usize, compile_compute_cap: usize) -> bool {
@@ -49,10 +34,13 @@ fn compute_cap_matching(runtime_compute_cap: usize, compile_compute_cap: usize)
     }
 }
 
-pub fn incompatible_compute_cap() -> bool {
-    let compile_compute_cap = get_compile_compute_cap();
-    let runtime_compute_cap = get_runtime_compute_cap();
-    !compute_cap_matching(runtime_compute_cap, compile_compute_cap)
+pub fn compatible_compute_cap() -> Result<bool, anyhow::Error> {
+    let compile_compute_cap = get_compile_compute_cap()?;
+    let runtime_compute_cap = get_runtime_compute_cap()?;
+    Ok(compute_cap_matching(
+        runtime_compute_cap,
+        compile_compute_cap,
+    ))
 }
 
 #[cfg(test)]
diff --git a/backends/candle/src/flash_attn.rs b/backends/candle/src/flash_attn.rs
index 556fd375..f2016928 100644
--- a/backends/candle/src/flash_attn.rs
+++ b/backends/candle/src/flash_attn.rs
@@ -1,5 +1,23 @@
-use crate::compute_cap::get_runtime_compute_cap;
 use candle::Tensor;
+use std::sync::Once;
+
+static INIT: Once = Once::new();
+static mut RUNTIME_COMPUTE_CAP: usize = 0;
+fn init_runtime_compute_cap() {
+    unsafe {
+        INIT.call_once(|| {
+            use crate::compute_cap::get_runtime_compute_cap;
+            RUNTIME_COMPUTE_CAP = get_runtime_compute_cap().unwrap();
+        });
+    }
+}
+
+pub fn get_runtime_compute_cap() -> usize {
+    unsafe {
+        init_runtime_compute_cap();
+        RUNTIME_COMPUTE_CAP
+    }
+}
 
 #[allow(clippy::too_many_arguments, unused)]
 pub(crate) fn flash_attn_varlen(
@@ -13,6 +31,7 @@ pub(crate) fn flash_attn_varlen(
     max_seqlen_k: usize,
     softmax_scale: f32,
     causal: bool,
+    window_size_left: Option<usize>,
 ) -> Result<Tensor, candle::Error> {
     let runtime_compute_cap = get_runtime_compute_cap();
 
@@ -20,6 +39,9 @@ pub(crate) fn flash_attn_varlen(
         if alibi_slopes.is_some() {
             candle::bail!("Flash attention v1 does not support alibi");
         }
+        if window_size_left.is_some() {
+            candle::bail!("Flash attention v1 does not support attention windowing");
+        }
 
         #[cfg(feature = "flash-attn-v1")]
         {
@@ -41,10 +63,12 @@ pub(crate) fn flash_attn_varlen(
     } else if (80..90).contains(&runtime_compute_cap) || runtime_compute_cap == 90 {
         #[cfg(feature = "flash-attn")]
         {
-            use candle_flash_attn::{flash_attn_varlen, flash_attn_varlen_alibi};
+            use candle_flash_attn::{flash_attn_varlen_alibi_windowed, flash_attn_varlen_windowed};
+
+            let window_size_right = if causal { Some(0) } else { None };
 
             let attention = if let Some(alibi_slopes) = alibi_slopes {
-                flash_attn_varlen_alibi(
+                flash_attn_varlen_alibi_windowed(
                     q,
                     k,
                     v,
@@ -54,10 +78,11 @@ pub(crate) fn flash_attn_varlen(
                     max_seqlen_q,
                     max_seqlen_k,
                     softmax_scale,
-                    causal,
+                    window_size_left,
+                    window_size_right,
                 )
             } else {
-                flash_attn_varlen(
+                flash_attn_varlen_windowed(
                     q,
                     k,
                     v,
@@ -66,7 +91,8 @@ pub(crate) fn flash_attn_varlen(
                     max_seqlen_q,
                     max_seqlen_k,
                     softmax_scale,
-                    causal,
+                    window_size_left,
+                    window_size_right,
                 )
             };
 
diff --git a/backends/candle/src/layers/cublaslt.rs b/backends/candle/src/layers/cublaslt.rs
index e35601e8..bfa2d182 100644
--- a/backends/candle/src/layers/cublaslt.rs
+++ b/backends/candle/src/layers/cublaslt.rs
@@ -11,21 +11,27 @@ static mut CUBLASLT: Option<CublasLtWrapper> = None;
 pub fn get_cublas_lt_wrapper() -> Option<&'static CublasLtWrapper> {
     unsafe {
         INIT.call_once(|| {
-            CUBLASLT = match Device::cuda_if_available(0) {
-                Ok(device) => {
-                    #[cfg(feature = "cuda")]
-                    {
-                        Some(CublasLtWrapper {
+            #[cfg(not(feature = "cuda"))]
+            {
+                CUBLASLT = None;
+            }
+
+            #[cfg(feature = "cuda")]
+            {
+                // Check if we can call the driver
+                // Then check if we can create a device
+                // Then check that the device is CUDA
+                use candle::cuda_backend::cudarc::driver;
+                CUBLASLT = driver::result::init()
+                    .ok()
+                    .and_then(|_| Device::cuda_if_available(0).ok())
+                    .and_then(|device| match device {
+                        Device::Cuda(_) => Some(CublasLtWrapper {
                             cublaslt: CublasLt::new(&device).unwrap(),
-                        })
-                    }
-                    #[cfg(not(feature = "cuda"))]
-                    {
-                        None
-                    }
-                }
-                Err(_) => None,
-            };
+                        }),
+                        _ => None,
+                    });
+            }
         });
         CUBLASLT.as_ref()
     }
diff --git a/backends/candle/src/layers/linear.rs b/backends/candle/src/layers/linear.rs
index 3fdd025b..fc8af1dd 100644
--- a/backends/candle/src/layers/linear.rs
+++ b/backends/candle/src/layers/linear.rs
@@ -7,6 +7,7 @@ use serde::Deserialize;
 pub enum HiddenAct {
     Gelu,
     Relu,
+    #[serde(alias = "silu")]
     Swiglu,
 }
 
diff --git a/backends/candle/src/layers.rs b/backends/candle/src/layers/mod.rs
similarity index 65%
rename from backends/candle/src/layers.rs
rename to backends/candle/src/layers/mod.rs
index 8e108fc2..81f63310 100644
--- a/backends/candle/src/layers.rs
+++ b/backends/candle/src/layers/mod.rs
@@ -2,7 +2,11 @@
 mod cublaslt;
 mod layer_norm;
 mod linear;
+#[allow(dead_code, unused)]
+mod rms_norm;
 
 pub use cublaslt::get_cublas_lt_wrapper;
 pub use layer_norm::LayerNorm;
 pub use linear::{HiddenAct, Linear};
+#[allow(unused_imports)]
+pub use rms_norm::RMSNorm;
diff --git a/backends/candle/src/layers/rms_norm.rs b/backends/candle/src/layers/rms_norm.rs
new file mode 100644
index 00000000..e7dab642
--- /dev/null
+++ b/backends/candle/src/layers/rms_norm.rs
@@ -0,0 +1,96 @@
+use candle::{DType, Device, Result, Tensor, D};
+use candle_nn::VarBuilder;
+
+#[derive(Debug)]
+pub struct RMSNorm {
+    weight: Tensor,
+    epsilon: f32,
+    span: tracing::Span,
+}
+
+impl RMSNorm {
+    pub fn load(vb: VarBuilder, hidden_size: usize, epsilon: f32) -> Result<Self> {
+        Ok(Self {
+            weight: vb
+                .get(hidden_size, "weight")
+                .or_else(|_| vb.get(hidden_size, "gamma"))?,
+            epsilon,
+            span: tracing::span!(tracing::Level::TRACE, "rms-norm"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        residual: Option<&Tensor>,
+    ) -> Result<(Tensor, Tensor)> {
+        let _enter = self.span.enter();
+
+        match hidden_states.device() {
+            Device::Cpu | Device::Metal(_) => {
+                let mut hidden_states = hidden_states.clone();
+                let residual_add = if let Some(residual) = residual {
+                    let residual_add = hidden_states.add(residual)?;
+                    hidden_states = residual_add.clone();
+                    residual_add
+                } else {
+                    hidden_states.clone()
+                };
+
+                let hidden_states_dtype = hidden_states.dtype();
+                let internal_dtype = match hidden_states_dtype {
+                    DType::F16 | DType::BF16 => DType::F32,
+                    d => d,
+                };
+                let hidden_size = hidden_states.dim(D::Minus1)?;
+                let hidden_states = hidden_states.to_dtype(internal_dtype)?;
+                let norm_hidden_states =
+                    (hidden_states.sqr()?.sum_keepdim(D::Minus1)? / hidden_size as f64)?;
+                let hidden_states_normed = hidden_states
+                    .broadcast_div(&(norm_hidden_states + self.epsilon as f64)?.sqrt()?)?;
+                Ok((
+                    hidden_states_normed
+                        .to_dtype(hidden_states_dtype)?
+                        .broadcast_mul(&self.weight)?,
+                    residual_add,
+                ))
+            }
+            Device::Cuda(_) => {
+                #[cfg(feature = "cuda")]
+                {
+                    use candle_layer_norm::{fused_add_rms_norm, rms_norm};
+
+                    let original_shape = hidden_states.shape();
+                    let hidden_states = hidden_states.flatten_to(D::Minus2)?;
+
+                    if let Some(residual) = residual {
+                        let residual = residual.flatten_to(D::Minus2)?;
+
+                        let (result, residual_add) = fused_add_rms_norm(
+                            &hidden_states,
+                            &residual,
+                            &self.weight,
+                            None,
+                            self.epsilon,
+                        )?;
+                        Ok((
+                            result.reshape(original_shape)?,
+                            residual_add.reshape(original_shape)?,
+                        ))
+                    } else {
+                        let residual_add = hidden_states.clone();
+
+                        let result = rms_norm(&hidden_states, &self.weight, None, self.epsilon)?;
+
+                        Ok((
+                            result.reshape(original_shape)?,
+                            residual_add.reshape(original_shape)?,
+                        ))
+                    }
+                }
+                #[cfg(not(feature = "cuda"))]
+                candle::bail!("`cuda` feature is not enabled")
+            }
+        }
+    }
+}
diff --git a/backends/candle/src/lib.rs b/backends/candle/src/lib.rs
index 9b9ab832..acd93750 100644
--- a/backends/candle/src/lib.rs
+++ b/backends/candle/src/lib.rs
@@ -8,19 +8,20 @@ mod models;
 
 #[cfg(feature = "cuda")]
 use crate::compute_cap::{
-    get_compile_compute_cap, get_runtime_compute_cap, incompatible_compute_cap,
+    compatible_compute_cap, get_compile_compute_cap, get_runtime_compute_cap,
 };
 use crate::models::{
-    BertModel, DistilBertConfig, DistilBertModel, JinaBertModel, Model, NomicBertModel,
-    NomicConfig, PositionEmbeddingType,
+    BertConfig, BertModel, DistilBertConfig, DistilBertModel, GTEConfig, JinaBertModel,
+    JinaCodeBertModel, MistralConfig, Model, NomicBertModel, NomicConfig, Qwen2Config,
 };
 #[cfg(feature = "cuda")]
 use crate::models::{
-    FlashBertModel, FlashDistilBertModel, FlashJinaBertModel, FlashNomicBertModel,
+    FlashBertModel, FlashDistilBertModel, FlashGTEModel, FlashJinaBertModel,
+    FlashJinaCodeBertModel, FlashMistralModel, FlashNomicBertModel, FlashQwen2Model,
 };
+use anyhow::Context;
 use candle::{DType, Device};
 use candle_nn::VarBuilder;
-use models::BertConfig;
 use nohash_hasher::BuildNoHashHasher;
 use serde::Deserialize;
 use std::collections::HashMap;
@@ -29,10 +30,25 @@ use text_embeddings_backend_core::{
     Backend, BackendError, Batch, Embedding, Embeddings, ModelType, Predictions,
 };
 
+/// This enum is needed to be able to differentiate between jina models that also use
+/// the `bert` model type and valid Bert models.
+/// We use the `_name_or_path` field in the config to do so. This might not be robust in the long
+/// run but is still better than the other options...
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+#[serde(tag = "_name_or_path")]
+pub enum BertConfigWrapper {
+    #[serde(rename = "jinaai/jina-bert-implementation")]
+    JinaBert(BertConfig),
+    #[serde(rename = "jinaai/jina-bert-v2-qk-post-norm")]
+    JinaCodeBert(BertConfig),
+    #[serde(untagged)]
+    Bert(BertConfig),
+}
+
 #[derive(Deserialize)]
 #[serde(tag = "model_type", rename_all = "kebab-case")]
 enum Config {
-    Bert(BertConfig),
+    Bert(BertConfigWrapper),
     XlmRoberta(BertConfig),
     Camembert(BertConfig),
     Roberta(BertConfig),
@@ -40,9 +56,14 @@ enum Config {
     DistilBert(DistilBertConfig),
     #[serde(rename(deserialize = "nomic_bert"))]
     NomicBert(NomicConfig),
+    Mistral(MistralConfig),
+    #[serde(rename = "new")]
+    Gte(GTEConfig),
+    Qwen2(Qwen2Config),
 }
 
 pub struct CandleBackend {
+    device: Device,
     model: Box<dyn Model + Send>,
 }
 
@@ -52,23 +73,82 @@ impl CandleBackend {
         dtype: String,
         model_type: ModelType,
     ) -> Result<Self, BackendError> {
+        // Default files
+        let default_safetensors = model_path.join("model.safetensors");
+        let default_pytorch = model_path.join("pytorch_model.bin");
+
+        // Single Files
+        let model_files = if default_safetensors.exists() {
+            vec![default_safetensors]
+        } else if default_pytorch.exists() {
+            vec![default_pytorch]
+        }
+        // Sharded weights
+        else {
+            // Get index file
+            let index_file = model_path.join("model.safetensors.index.json");
+
+            // Parse file
+            let index_file_string: String = std::fs::read_to_string(&index_file)
+                .map_err(|err| BackendError::Start(err.to_string()))?;
+            let json: serde_json::Value = serde_json::from_str(&index_file_string)
+                .map_err(|err| BackendError::Start(err.to_string()))?;
+
+            let weight_map = match json.get("weight_map") {
+                None => {
+                    return Err(BackendError::Start(format!(
+                        "no weight map in {index_file:?}"
+                    )));
+                }
+                Some(serde_json::Value::Object(map)) => map,
+                Some(_) => {
+                    return Err(BackendError::Start(format!(
+                        "weight map in {index_file:?} is not a map"
+                    )));
+                }
+            };
+            let mut safetensors_files = std::collections::HashSet::new();
+            for value in weight_map.values() {
+                if let Some(file) = value.as_str() {
+                    safetensors_files.insert(file.to_string());
+                }
+            }
+
+            // Collect paths
+            safetensors_files
+                .iter()
+                .map(|n| model_path.join(n))
+                .collect()
+        };
+
         // Load config
         let config: String = std::fs::read_to_string(model_path.join("config.json"))
-            .map_err(|err| BackendError::Start(err.to_string()))?;
+            .context("Unable to read config file")
+            .map_err(|err| BackendError::Start(format!("{err:?}")))?;
         let config: Config = serde_json::from_str(&config)
-            .map_err(|err| BackendError::Start(format!("Model is not supported: {}", err)))?;
+            .context("Model is not supported")
+            .map_err(|err| BackendError::Start(format!("{err:?}")))?;
 
         // Get candle device
         let device = if candle::utils::cuda_is_available() {
             #[cfg(feature = "cuda")]
-            if incompatible_compute_cap() {
-                return Err(BackendError::Start(format!(
-                    "Runtime compute cap {} is not compatible with compile time compute cap {}",
-                    get_runtime_compute_cap(),
-                    get_compile_compute_cap()
-                )));
+            match compatible_compute_cap() {
+                Ok(true) => Device::new_cuda(0),
+                Ok(false) => {
+                    return Err(BackendError::Start(format!(
+                        "Runtime compute cap {} is not compatible with compile time compute cap {}",
+                        get_runtime_compute_cap().unwrap(),
+                        get_compile_compute_cap().unwrap()
+                    )));
+                }
+                Err(err) => {
+                    tracing::warn!("Could not find a compatible CUDA device on host: {err:?}");
+                    tracing::warn!("Using CPU instead");
+                    Ok(Device::Cpu)
+                }
             }
-            Device::new_cuda(0)
+            #[cfg(not(feature = "cuda"))]
+            Ok(Device::Cpu)
         } else if candle::utils::metal_is_available() {
             Device::new_metal(0)
         } else {
@@ -87,17 +167,10 @@ impl CandleBackend {
             )))
         }?;
 
-        let safetensors_path = model_path.join("model.safetensors");
-        let vb = if safetensors_path.exists() {
-            unsafe {
-                VarBuilder::from_mmaped_safetensors(
-                    &[model_path.join("model.safetensors")],
-                    dtype,
-                    &device,
-                )
-            }
+        let vb = if model_files.len() == 1 && model_files[0].extension().unwrap() == "bin" {
+            VarBuilder::from_pth(&model_files[0], dtype, &device)
         } else {
-            VarBuilder::from_pth(model_path.join("pytorch_model.bin"), dtype, &device)
+            unsafe { VarBuilder::from_mmaped_safetensors(&model_files, dtype, &device) }
         }
         .s()?;
 
@@ -106,15 +179,22 @@ impl CandleBackend {
             (_, Device::Cuda(_)) => Err(BackendError::Start(
                 "`cuda` feature is not enabled".to_string(),
             )),
-            (Config::Bert(config), Device::Cpu | Device::Metal(_)) => {
-                if config.position_embedding_type == PositionEmbeddingType::Alibi {
-                    tracing::info!("Starting JinaBertModel model on {:?}", device);
+            (Config::Bert(config), Device::Cpu | Device::Metal(_)) => match config {
+                BertConfigWrapper::JinaBert(config) => {
+                    tracing::info!("Starting JinaBert model on {:?}", device);
                     Ok(Box::new(JinaBertModel::load(vb, &config, model_type).s()?))
-                } else {
+                }
+                BertConfigWrapper::JinaCodeBert(config) => {
+                    tracing::info!("Starting JinaCodeBert model on {:?}", device);
+                    Ok(Box::new(
+                        JinaCodeBertModel::load(vb, &config, model_type).s()?,
+                    ))
+                }
+                BertConfigWrapper::Bert(config) => {
                     tracing::info!("Starting Bert model on {:?}", device);
                     Ok(Box::new(BertModel::load(vb, &config, model_type).s()?))
                 }
-            }
+            },
             (
                 Config::XlmRoberta(config) | Config::Camembert(config) | Config::Roberta(config),
                 Device::Cpu | Device::Metal(_),
@@ -125,40 +205,69 @@ impl CandleBackend {
                 ))
             }
             (Config::DistilBert(config), Device::Cpu | Device::Metal(_)) => {
-                tracing::info!("Starting DistilBertModel model on {:?}", device);
+                tracing::info!("Starting DistilBert model on {:?}", device);
                 Ok(Box::new(
                     DistilBertModel::load(vb, &config, model_type).s()?,
                 ))
             }
             (Config::NomicBert(config), Device::Cpu | Device::Metal(_)) => {
-                tracing::info!("Starting NomicBertModel model on {:?}", device);
+                tracing::info!("Starting NomicBert model on {:?}", device);
                 Ok(Box::new(NomicBertModel::load(vb, &config, model_type).s()?))
             }
+            (Config::Mistral(_), Device::Cpu | Device::Metal(_)) => Err(BackendError::Start(
+                "Mistral is only supported on Cuda devices in fp16 with flash attention enabled"
+                    .to_string(),
+            )),
+            (Config::Gte(_), Device::Cpu | Device::Metal(_)) => Err(BackendError::Start(
+                "GTE is only supported on Cuda devices in fp16 with flash attention enabled"
+                    .to_string(),
+            )),
+            (Config::Qwen2(_), Device::Cpu | Device::Metal(_)) => Err(BackendError::Start(
+                "Qwen2 is only supported on Cuda devices in fp16 with flash attention enabled"
+                    .to_string(),
+            )),
             #[cfg(feature = "cuda")]
             (Config::Bert(config), Device::Cuda(_)) => {
                 if cfg!(any(feature = "flash-attn", feature = "flash-attn-v1"))
                     && dtype == DType::F16
-                    && ((config.position_embedding_type == PositionEmbeddingType::Absolute) | (config.position_embedding_type == PositionEmbeddingType::Alibi))
                     // Allow disabling because of flash attention v1 precision problems
                     // See: https://github.com/huggingface/text-embeddings-inference/issues/37
                     && &std::env::var("USE_FLASH_ATTENTION").unwrap_or("True".to_string()).to_lowercase() == "true"
                 {
-                    if config.position_embedding_type == PositionEmbeddingType::Alibi {
-                        tracing::info!("Starting FlashJinaBertModel model on {:?}", device);
-                        Ok(Box::new(
-                            FlashJinaBertModel::load(vb, &config, model_type).s()?,
-                        ))
-                    } else {
-                        tracing::info!("Starting FlashBert model on {:?}", device);
-                        Ok(Box::new(FlashBertModel::load(vb, &config, model_type).s()?))
+                    match config {
+                        BertConfigWrapper::JinaBert(config) => {
+                            tracing::info!("Starting FlashJinaBert model on {:?}", device);
+                            Ok(Box::new(
+                                FlashJinaBertModel::load(vb, &config, model_type).s()?,
+                            ))
+                        }
+                        BertConfigWrapper::JinaCodeBert(config) => {
+                            tracing::info!("Starting FlashJinaCodeBert model on {:?}", device);
+                            Ok(Box::new(
+                                FlashJinaCodeBertModel::load(vb, &config, model_type).s()?,
+                            ))
+                        }
+                        BertConfigWrapper::Bert(config) => {
+                            tracing::info!("Starting FlashBert model on {:?}", device);
+                            Ok(Box::new(FlashBertModel::load(vb, &config, model_type).s()?))
+                        }
                     }
                 } else {
-                    if config.position_embedding_type == PositionEmbeddingType::Alibi {
-                        tracing::info!("Starting JinaBertModel model on {:?}", device);
-                        Ok(Box::new(JinaBertModel::load(vb, &config, model_type).s()?))
-                    } else {
-                        tracing::info!("Starting Bert model on {:?}", device);
-                        Ok(Box::new(BertModel::load(vb, &config, model_type).s()?))
+                    match config {
+                        BertConfigWrapper::JinaBert(config) => {
+                            tracing::info!("Starting JinaBert model on {:?}", device);
+                            Ok(Box::new(JinaBertModel::load(vb, &config, model_type).s()?))
+                        }
+                        BertConfigWrapper::JinaCodeBert(config) => {
+                            tracing::info!("Starting JinaCodeBert model on {:?}", device);
+                            Ok(Box::new(
+                                JinaCodeBertModel::load(vb, &config, model_type).s()?,
+                            ))
+                        }
+                        BertConfigWrapper::Bert(config) => {
+                            tracing::info!("Starting Bert model on {:?}", device);
+                            Ok(Box::new(BertModel::load(vb, &config, model_type).s()?))
+                        }
                     }
                 }
             }
@@ -169,7 +278,6 @@ impl CandleBackend {
             ) => {
                 if cfg!(any(feature = "flash-attn", feature = "flash-attn-v1"))
                     && dtype == DType::F16
-                    && ((config.position_embedding_type == PositionEmbeddingType::Absolute) | (config.position_embedding_type == PositionEmbeddingType::Alibi))
                     // Allow disabling because of flash attention v1 precision problems
                     // See: https://github.com/huggingface/text-embeddings-inference/issues/37
                     && &std::env::var("USE_FLASH_ATTENTION").unwrap_or("True".to_string()).to_lowercase() == "true"
@@ -194,7 +302,7 @@ impl CandleBackend {
                         .to_lowercase()
                         == "true"
                 {
-                    tracing::info!("Starting FlashDistilBertModel model on {:?}", device);
+                    tracing::info!("Starting FlashDistilBert model on {:?}", device);
                     Ok(Box::new(
                         FlashDistilBertModel::load(vb, &config, model_type).s()?,
                     ))
@@ -214,22 +322,68 @@ impl CandleBackend {
                         .to_lowercase()
                         == "true"
                 {
-                    tracing::info!("Starting FlashNomicBertModel model on {:?}", device);
+                    tracing::info!("Starting FlashNomicBert model on {:?}", device);
                     Ok(Box::new(
                         FlashNomicBertModel::load(vb, &config, model_type).s()?,
                     ))
                 } else {
-                    tracing::info!("Starting NomicBertModel model on {:?}", device);
+                    tracing::info!("Starting NomicBert model on {:?}", device);
                     Ok(Box::new(NomicBertModel::load(vb, &config, model_type).s()?))
                 }
             }
+            #[cfg(feature = "cuda")]
+            (Config::Mistral(config), Device::Cuda(_)) => {
+                if dtype != DType::F16
+                    || !cfg!(feature = "flash-attn")
+                    || get_runtime_compute_cap().unwrap() < 80
+                {
+                    return Err(BackendError::Start("Mistral is only supported on Cuda devices in fp16 with flash attention v2 enabled".to_string()));
+                }
+                tracing::info!("Starting FlashMistral model on {:?}", device);
+                Ok(Box::new(
+                    FlashMistralModel::load(vb, &config, model_type).s()?,
+                ))
+            }
+            #[cfg(feature = "cuda")]
+            (Config::Gte(config), Device::Cuda(_)) => {
+                if dtype != DType::F16
+                    || !cfg!(any(feature = "flash-attn", feature = "flash-attn-v1"))
+                {
+                    return Err(BackendError::Start("GTE is only supported on Cuda devices in fp16 with flash attention enabled".to_string()));
+                }
+                tracing::info!("Starting FlashGTE model on {:?}", device);
+                Ok(Box::new(FlashGTEModel::load(vb, &config, model_type).s()?))
+            }
+            #[cfg(feature = "cuda")]
+            (Config::Qwen2(config), Device::Cuda(_)) => {
+                if dtype != DType::F16
+                    || !cfg!(any(feature = "flash-attn", feature = "flash-attn-v1"))
+                {
+                    return Err(BackendError::Start("Qwen2 is only supported on Cuda devices in fp16 with flash attention v2 enabled".to_string()));
+                }
+                tracing::info!("Starting FlashQwen2 model on {:?}", device);
+                Ok(Box::new(
+                    FlashQwen2Model::load(vb, &config, model_type).s()?,
+                ))
+            }
         };
 
-        Ok(Self { model: model? })
+        Ok(Self {
+            device,
+            model: model?,
+        })
     }
 }
 
 impl Backend for CandleBackend {
+    fn max_batch_size(&self) -> Option<usize> {
+        // Limit max batch size to 4 on CPU
+        if matches!(self.device, Device::Cpu) {
+            return Some(4);
+        }
+        None
+    }
+
     fn health(&self) -> Result<(), BackendError> {
         Ok(())
     }
diff --git a/backends/candle/src/models/bert.rs b/backends/candle/src/models/bert.rs
index 200e41de..a36a1555 100644
--- a/backends/candle/src/models/bert.rs
+++ b/backends/candle/src/models/bert.rs
@@ -35,6 +35,7 @@ pub enum PositionEmbeddingType {
     #[default]
     Absolute,
     Alibi,
+    Rope,
 }
 
 #[derive(Debug)]
@@ -405,13 +406,14 @@ impl ClassificationHead for BertClassificationHead {
     fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
         let _enter = self.span.enter();
 
-        let mut hidden_states = hidden_states.clone();
+        let mut hidden_states = hidden_states.unsqueeze(1)?;
         if let Some(pooler) = self.pooler.as_ref() {
             hidden_states = pooler.forward(&hidden_states)?;
             hidden_states = hidden_states.tanh()?;
         }
 
         let hidden_states = self.output.forward(&hidden_states)?;
+        let hidden_states = hidden_states.squeeze(1)?;
         Ok(hidden_states)
     }
 }
@@ -453,10 +455,11 @@ impl ClassificationHead for RobertaClassificationHead {
     fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
         let _enter = self.span.enter();
 
-        let hidden_states = self.intermediate.forward(hidden_states)?;
+        let hidden_states = hidden_states.unsqueeze(1)?;
+        let hidden_states = self.intermediate.forward(&hidden_states)?;
         let hidden_states = hidden_states.tanh()?;
         let hidden_states = self.output.forward(&hidden_states)?;
-
+        let hidden_states = hidden_states.squeeze(1)?;
         Ok(hidden_states)
     }
 }
@@ -636,6 +639,10 @@ impl BertModel {
                 (pool, Some(classifier), None)
             }
             ModelType::Embedding(pool) => {
+                if pool == Pool::LastToken {
+                    candle::bail!("`last_token` is not supported for Bert");
+                }
+
                 let splade = if pool == Pool::Splade {
                     Some(BertSpladeHead::load_roberta(vb.clone(), config)?)
                 } else {
@@ -830,6 +837,8 @@ impl BertModel {
             let pooled_embeddings = match self.pool {
                 // CLS pooling
                 Pool::Cls => outputs.i((.., 0))?,
+                // Last token pooling is not supported for this model
+                Pool::LastToken => unreachable!(),
                 // Mean pooling
                 Pool::Mean => {
                     if let Some(ref attention_mask) = attention_mask {
diff --git a/backends/candle/src/models/distilbert.rs b/backends/candle/src/models/distilbert.rs
index e7145309..2cf62081 100644
--- a/backends/candle/src/models/distilbert.rs
+++ b/backends/candle/src/models/distilbert.rs
@@ -389,7 +389,12 @@ impl DistilBertModel {
             ModelType::Classifier => {
                 candle::bail!("`classifier` model type is not supported for DistilBert")
             }
-            ModelType::Embedding(pool) => pool,
+            ModelType::Embedding(pool) => {
+                if pool == Pool::LastToken {
+                    candle::bail!("`last_token` is not supported for DistilBert");
+                }
+                pool
+            }
         };
 
         let (embeddings, encoder) = match (
@@ -564,6 +569,8 @@ impl DistilBertModel {
             let pooled_embeddings = match self.pool {
                 // CLS pooling
                 Pool::Cls => outputs.i((.., 0))?,
+                // Last token pooling is not supported for this model
+                Pool::LastToken => unreachable!(),
                 // Mean pooling
                 Pool::Mean => {
                     if let Some(ref attention_mask) = attention_mask {
diff --git a/backends/candle/src/models/flash_bert.rs b/backends/candle/src/models/flash_bert.rs
index d248c91f..8951dcc1 100644
--- a/backends/candle/src/models/flash_bert.rs
+++ b/backends/candle/src/models/flash_bert.rs
@@ -5,7 +5,7 @@ use crate::models::bert::{
     PositionEmbeddingType, RobertaClassificationHead,
 };
 use crate::models::Model;
-use candle::{DType, Device, Result, Tensor};
+use candle::{DType, Device, IndexOp, Result, Tensor};
 use candle_nn::VarBuilder;
 use text_embeddings_backend_core::{Batch, ModelType, Pool};
 
@@ -103,6 +103,7 @@ impl BertAttention {
             max_s,
             self.softmax_scale,
             false,
+            None,
         )?;
         let attention = attention.flatten_from(candle::D::Minus2)?;
 
@@ -393,27 +394,47 @@ impl FlashBertModel {
 
         let pooled_embeddings = if has_pooling_requests {
             match self.pool {
-                // CLS pooling
-                Pool::Cls => {
-                    // Get the indices of the cls tokens from cu_seqlens
-                    let mut cls_indices = cu_seqlens.narrow(0, 0, batch_size)?;
-
-                    // If raw_indices is empty, we don't need to do anything with
-                    // the pooled_indices
-                    if has_raw_requests {
-                        // We need the pooled indices to select the correct cls indices
-                        let pooled_indices = Tensor::from_vec(
-                            batch.pooled_indices.clone(),
-                            batch.pooled_indices.len(),
-                            &self.device,
-                        )?;
-
-                        // Only select indices that requires pooling
-                        cls_indices = cls_indices.index_select(&pooled_indices, 0)?
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
+                    if batch_size > 1 {
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
+
+                        // If raw_indices is empty, we don't need to do anything with
+                        // the pooled_indices
+                        if has_raw_requests {
+                            // We need the pooled indices to select the correct cls indices
+                            let pooled_indices = Tensor::from_vec(
+                                batch.pooled_indices.clone(),
+                                batch.pooled_indices.len(),
+                                &self.device,
+                            )?;
+
+                            // Only select indices that requires pooling
+                            indices = indices.index_select(&pooled_indices, 0)?
+                        }
+
+                        // Select tokens
+                        Some(outputs.index_select(&indices, 0)?)
+                    } else {
+                        Some(
+                            match self.pool {
+                                Pool::Cls => outputs.i(0)?,
+                                Pool::LastToken => {
+                                    outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                                }
+                                _ => unreachable!(),
+                            }
+                            .unsqueeze(0)?,
+                        )
                     }
-
-                    // Select cls tokens
-                    Some(outputs.index_select(&cls_indices, 0)?)
                 }
                 // Mean pooling
                 Pool::Mean => {
diff --git a/backends/candle/src/models/flash_distilbert.rs b/backends/candle/src/models/flash_distilbert.rs
index 3d30c3a8..7f060601 100644
--- a/backends/candle/src/models/flash_distilbert.rs
+++ b/backends/candle/src/models/flash_distilbert.rs
@@ -4,7 +4,7 @@ use crate::models::distilbert::{
     DistilBertConfig, DistilBertEmbeddings, DistilBertMLP, DistilBertSpladeHead,
 };
 use crate::models::Model;
-use candle::{DType, Device, Result, Tensor};
+use candle::{DType, Device, IndexOp, Result, Tensor};
 use candle_nn::VarBuilder;
 use text_embeddings_backend_core::{Batch, ModelType, Pool};
 
@@ -84,6 +84,7 @@ impl DistilBertAttention {
             max_s,
             self.softmax_scale,
             false,
+            None,
         )?;
         let attention = attention.flatten_from(candle::D::Minus2)?;
 
@@ -259,27 +260,45 @@ impl FlashDistilBertModel {
 
         let pooled_embeddings = if has_pooling_requests {
             let pooled_embeddings = match self.pool {
-                // CLS pooling
-                Pool::Cls => {
-                    // Get the indices of the cls tokens from cu_seqlens
-                    let mut cls_indices = cu_seqlens.narrow(0, 0, batch_size)?;
-
-                    // If raw_indices is empty, we don't need to do anything with
-                    // the pooled_indices
-                    if has_raw_requests {
-                        // We need the pooled indices to select the correct cls indices
-                        let pooled_indices = Tensor::from_vec(
-                            batch.pooled_indices.clone(),
-                            batch.pooled_indices.len(),
-                            &self.device,
-                        )?;
-
-                        // Only select indices that requires pooling
-                        cls_indices = cls_indices.index_select(&pooled_indices, 0)?
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
+                    if batch_size > 1 {
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
+
+                        // If raw_indices is empty, we don't need to do anything with
+                        // the pooled_indices
+                        if has_raw_requests {
+                            // We need the pooled indices to select the correct cls indices
+                            let pooled_indices = Tensor::from_vec(
+                                batch.pooled_indices.clone(),
+                                batch.pooled_indices.len(),
+                                &self.device,
+                            )?;
+
+                            // Only select indices that requires pooling
+                            indices = indices.index_select(&pooled_indices, 0)?
+                        }
+
+                        // Select tokens
+                        outputs.index_select(&indices, 0)?
+                    } else {
+                        match self.pool {
+                            Pool::Cls => outputs.i(0)?,
+                            Pool::LastToken => {
+                                outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                            }
+                            _ => unreachable!(),
+                        }
+                        .unsqueeze(0)?
                     }
-
-                    // Select cls tokens
-                    outputs.index_select(&cls_indices, 0)?
                 }
                 // Mean pooling
                 Pool::Mean => {
diff --git a/backends/candle/src/models/flash_gte.rs b/backends/candle/src/models/flash_gte.rs
new file mode 100644
index 00000000..f3aec220
--- /dev/null
+++ b/backends/candle/src/models/flash_gte.rs
@@ -0,0 +1,463 @@
+use crate::flash_attn::flash_attn_varlen;
+use crate::layers::{HiddenAct, LayerNorm, Linear};
+use crate::models::{GTEConfig, Model, NTKScaling, PositionEmbeddingType, RopeScaling};
+use candle::{DType, Device, IndexOp, Result, Tensor};
+use candle_nn::{Embedding, Module, VarBuilder};
+use text_embeddings_backend_core::{Batch, ModelType, Pool};
+
+struct GTEAttention {
+    qkv_linear: Linear,
+    o_proj: Linear,
+
+    num_attention_heads: usize,
+    attention_head_size: usize,
+
+    softmax_scale: f32,
+
+    span: tracing::Span,
+}
+
+impl GTEAttention {
+    pub fn load(vb: VarBuilder, config: &GTEConfig) -> Result<Self> {
+        let num_attention_heads = config.num_attention_heads;
+        let attention_head_size = config.hidden_size / config.num_attention_heads;
+        let hidden_size = config.hidden_size;
+
+        let qkv_weight = vb
+            .pp("qkv_proj")
+            .get((hidden_size * 3, hidden_size), "weight")?;
+        let qkv_bias = vb.pp("qkv_proj").get(hidden_size * 3, "bias")?;
+
+        let qkv_linear = Linear::new(qkv_weight, Some(qkv_bias), None);
+
+        let o_proj_weight = vb.pp("o_proj").get((hidden_size, hidden_size), "weight")?;
+        let o_proj_bias = vb.pp("o_proj").get(hidden_size, "bias")?;
+
+        let o_proj = Linear::new(o_proj_weight, Some(o_proj_bias), None);
+
+        let softmax_scale = (1. / (attention_head_size as f64).sqrt()) as f32;
+
+        Ok(Self {
+            qkv_linear,
+            o_proj,
+            num_attention_heads,
+            attention_head_size,
+            softmax_scale,
+            span: tracing::span!(tracing::Level::TRACE, "attention"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        cu_seqlens: &Tensor,
+        cos: &Tensor,
+        sin: &Tensor,
+        max_s: usize,
+    ) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let qkv = self.qkv_linear.forward(hidden_states)?;
+
+        // Reshape to [tokens, heads, head_size]
+        let mut new_qkv_shape = qkv.dims().to_vec();
+        new_qkv_shape.pop();
+        new_qkv_shape.push(self.num_attention_heads * 3);
+        new_qkv_shape.push(self.attention_head_size);
+
+        let qkv = qkv.reshape(new_qkv_shape)?;
+
+        // Split qkv tensor
+        let q = qkv.narrow(1, 0, self.num_attention_heads)?;
+        let k = qkv.narrow(1, self.num_attention_heads, self.num_attention_heads)?;
+        let v = qkv.narrow(1, self.num_attention_heads * 2, self.num_attention_heads)?;
+
+        candle_rotary::apply_rotary_inplace(&q, &k, &cos, &sin, true)?;
+
+        let attention = flash_attn_varlen(
+            &q,
+            &k,
+            &v,
+            None,
+            cu_seqlens,
+            cu_seqlens,
+            max_s,
+            max_s,
+            self.softmax_scale,
+            false,
+            None,
+        )?;
+        let attention = attention.flatten_from(candle::D::Minus2)?;
+
+        self.o_proj.forward(&attention)
+    }
+}
+
+struct GTEMLP {
+    up_gate_proj: Linear,
+    down_proj: Linear,
+
+    act: HiddenAct,
+    intermediate_size: usize,
+
+    span: tracing::Span,
+}
+
+impl GTEMLP {
+    pub fn load(vb: VarBuilder, config: &GTEConfig) -> Result<Self> {
+        let intermediate_size = config.intermediate_size;
+
+        let up_gate_proj_weight = vb
+            .pp("up_gate_proj")
+            .get((intermediate_size * 2, config.hidden_size), "weight")?;
+
+        let up_gate_proj = Linear::new(up_gate_proj_weight, None, None);
+
+        let down_proj_weight = vb
+            .pp("down_proj")
+            .get((config.hidden_size, intermediate_size), "weight")?;
+        let down_proj_bias = vb.pp("down_proj").get(config.hidden_size, "bias")?;
+        let down_proj = Linear::new(down_proj_weight, Some(down_proj_bias), None);
+
+        Ok(Self {
+            up_gate_proj,
+            down_proj,
+            intermediate_size,
+            act: config.hidden_act.clone(),
+            span: tracing::span!(tracing::Level::TRACE, "mlp"),
+        })
+    }
+
+    pub fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let up_gate_states = self.up_gate_proj.forward(hidden_states)?;
+        let up_states = up_gate_states.narrow(1, 0, self.intermediate_size)?;
+        let gate_states =
+            up_gate_states.narrow(1, self.intermediate_size, self.intermediate_size)?;
+
+        let gate_states = match self.act {
+            HiddenAct::Gelu => gate_states.gelu(),
+            HiddenAct::Relu => gate_states.relu(),
+            HiddenAct::Swiglu => gate_states.silu(),
+        }?;
+        let r = self.down_proj.forward(&(gate_states * up_states)?);
+        r
+    }
+}
+
+struct GTELayer {
+    attention: GTEAttention,
+    mlp: GTEMLP,
+    attention_layer_norm: LayerNorm,
+    mlp_layer_norm: LayerNorm,
+
+    span: tracing::Span,
+}
+
+impl GTELayer {
+    pub fn load(vb: VarBuilder, config: &GTEConfig) -> Result<Self> {
+        let attention = GTEAttention::load(vb.pp("attention"), config)?;
+        let mlp = GTEMLP::load(vb.pp("mlp"), config)?;
+
+        let attention_layer_norm =
+            LayerNorm::load(vb.pp("attn_ln"), config.hidden_size, config.layer_norm_eps)?;
+        let mlp_layer_norm =
+            LayerNorm::load(vb.pp("mlp_ln"), config.hidden_size, config.layer_norm_eps)?;
+
+        Ok(Self {
+            attention,
+            mlp,
+            attention_layer_norm,
+            mlp_layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "layer"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        cu_seqlens: &Tensor,
+        cos: &Tensor,
+        sin: &Tensor,
+        max_s: usize,
+    ) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let attn_output = self
+            .attention
+            .forward(&hidden_states, cu_seqlens, cos, sin, max_s)?;
+        let normed_attn_res_output = self
+            .attention_layer_norm
+            .forward(&attn_output, Some(hidden_states))?;
+
+        let mlp_output = self.mlp.forward(&normed_attn_res_output)?;
+        let normed_mlp_res_output = self
+            .mlp_layer_norm
+            .forward(&mlp_output, Some(&normed_attn_res_output))?;
+        Ok(normed_mlp_res_output)
+    }
+}
+
+pub struct FlashGTEModel {
+    word_embeddings: Embedding,
+    token_type_embeddings: Option<Embedding>,
+    layers: Vec<GTELayer>,
+    embeddings_norm: LayerNorm,
+    cos_cache: Tensor,
+    sin_cache: Tensor,
+    pool: Pool,
+    pub device: Device,
+
+    span: tracing::Span,
+}
+
+impl FlashGTEModel {
+    pub fn load(vb: VarBuilder, config: &GTEConfig, model_type: ModelType) -> Result<Self> {
+        match vb.device() {
+            Device::Cuda(_) => {}
+            _ => candle::bail!("FlashGTE requires Cuda"),
+        }
+
+        if vb.dtype() != DType::F16 {
+            candle::bail!("FlashGTE requires DType::F16")
+        }
+
+        if config.logn_attention_clip1 {
+            candle::bail!("`logn_attention_clip1` is not supported");
+        }
+        if config.logn_attention_scale {
+            candle::bail!("`logn_attention_scale` is not supported");
+        }
+
+        if config.position_embedding_type != PositionEmbeddingType::Rope {
+            candle::bail!("Only `PositionEmbeddingType::Rope` is supported");
+        }
+
+        let pool = match model_type {
+            ModelType::Classifier => {
+                candle::bail!("`classifier` model type is not supported for GTE")
+            }
+            ModelType::Embedding(pool) => pool,
+        };
+
+        let word_embeddings = Embedding::new(
+            vb.pp("embeddings.word_embeddings")
+                .get((config.vocab_size, config.hidden_size), "weight")?,
+            config.hidden_size,
+        );
+
+        let token_type_embeddings = if config.type_vocab_size > 0 {
+            Some(Embedding::new(
+                vb.pp("embeddings.token_type_embeddings")
+                    .get((config.type_vocab_size, config.hidden_size), "weight")?,
+                config.hidden_size,
+            ))
+        } else {
+            None
+        };
+
+        let layers = (0..config.num_hidden_layers)
+            .map(|index| GTELayer::load(vb.pp(format!("encoder.layer.{index}")), config))
+            .collect::<Result<Vec<_>>>()?;
+
+        let embeddings_norm = LayerNorm::load(
+            vb.pp("embeddings.LayerNorm"),
+            config.hidden_size,
+            config.layer_norm_eps,
+        )?;
+
+        let inv_freqs = if let Some(RopeScaling::Ntk(NTKScaling { factor })) = config.rope_scaling {
+            let inv_freqs = candle_rotary::inv_freqs(
+                layers[0].attention.attention_head_size,
+                config.rope_theta * factor,
+                vb.device(),
+            )?;
+            let s = factor.powf(2.0 / layers[0].attention.attention_head_size as f32) as f64;
+            inv_freqs / s
+        } else {
+            candle_rotary::inv_freqs(
+                layers[0].attention.attention_head_size,
+                config.rope_theta,
+                vb.device(),
+            )
+        }?;
+
+        let (cos_cache, sin_cache) =
+            candle_rotary::cos_sin(config.max_position_embeddings, &inv_freqs, vb.dtype())?;
+
+        Ok(Self {
+            word_embeddings,
+            token_type_embeddings,
+            layers,
+            embeddings_norm,
+            cos_cache,
+            sin_cache,
+            pool,
+            device: vb.device().clone(),
+            span: tracing::span!(tracing::Level::TRACE, "model"),
+        })
+    }
+
+    pub fn forward(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        let _enter = self.span.enter();
+
+        let batch_size = batch.cumulative_seq_lengths.len() - 1;
+        let shape = batch.input_ids.len();
+
+        // Create Cuda tensors
+        let input_ids = Tensor::from_vec(batch.input_ids, shape, &self.device)?;
+        let token_type_ids = Tensor::from_vec(batch.token_type_ids, shape, &self.device)?;
+        let position_ids = Tensor::from_vec(batch.position_ids, shape, &self.device)?;
+        let cu_seqlens = Tensor::from_vec(
+            batch.cumulative_seq_lengths.clone(),
+            batch_size + 1,
+            &self.device,
+        )?;
+
+        let word_embeddings = self.word_embeddings.forward(&input_ids)?;
+        let token_type_embeddings = self
+            .token_type_embeddings
+            .as_ref()
+            .map(|emb| emb.forward(&token_type_ids))
+            .transpose()?;
+
+        let mut hidden_states = self
+            .embeddings_norm
+            .forward(&word_embeddings, token_type_embeddings.as_ref())?;
+
+        let cos = self.cos_cache.index_select(&position_ids, 0)?;
+        let sin = self.sin_cache.index_select(&position_ids, 0)?;
+
+        for layer in &self.layers {
+            let h = layer.forward(
+                &hidden_states,
+                &cu_seqlens,
+                &cos,
+                &sin,
+                batch.max_length as usize,
+            )?;
+            hidden_states = h;
+        }
+
+        let outputs = hidden_states;
+
+        let has_pooling_requests = !batch.pooled_indices.is_empty();
+        let has_raw_requests = !batch.raw_indices.is_empty();
+
+        let pooled_embeddings = if has_pooling_requests {
+            match self.pool {
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
+                    if batch_size > 1 {
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
+
+                        // If raw_indices is empty, we don't need to do anything with
+                        // the pooled_indices
+                        if has_raw_requests {
+                            // We need the pooled indices to select the correct cls indices
+                            let pooled_indices = Tensor::from_vec(
+                                batch.pooled_indices.clone(),
+                                batch.pooled_indices.len(),
+                                &self.device,
+                            )?;
+
+                            // Only select indices that requires pooling
+                            indices = indices.index_select(&pooled_indices, 0)?
+                        }
+
+                        // Select tokens
+                        Some(outputs.index_select(&indices, 0)?)
+                    } else {
+                        Some(
+                            match self.pool {
+                                Pool::Cls => outputs.i(0)?,
+                                Pool::LastToken => {
+                                    outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                                }
+                                _ => unreachable!(),
+                            }
+                            .unsqueeze(0)?,
+                        )
+                    }
+                }
+                // Mean pooling
+                Pool::Mean => {
+                    if batch_size > 1 {
+                        // for each request that requires pooling
+                        let results: Result<Vec<Tensor>> = batch
+                            .pooled_indices
+                            .into_iter()
+                            .map(|i| {
+                                let i = i as usize;
+                                let start = batch.cumulative_seq_lengths[i];
+                                let len = batch.cumulative_seq_lengths[i + 1] - start;
+
+                                // Mean
+                                let embeddings = outputs.narrow(0, start as usize, len as usize)?;
+                                embeddings.sum_keepdim(0)? / (len as f64)
+                            })
+                            .collect();
+
+                        // Concatenate all results
+                        Some(Tensor::cat(&results?, 0)?)
+                    } else {
+                        Some((outputs.sum_keepdim(0)? / (batch.max_length as f64))?)
+                    }
+                }
+                Pool::Splade => {
+                    unreachable!();
+                }
+            }
+        } else {
+            None
+        };
+
+        let raw_embeddings = if has_raw_requests {
+            if batch_size > 1 && has_pooling_requests {
+                // Create indexing vector for the embeddings
+                let mut final_indices: Vec<u32> = Vec::with_capacity(shape);
+                for i in batch.raw_indices.into_iter() {
+                    let i = i as usize;
+                    // Get start/end token index of this specific member of the batch
+                    let start = batch.cumulative_seq_lengths[i];
+                    let end = batch.cumulative_seq_lengths[i + 1];
+
+                    for j in start..end {
+                        // Add indices for the tokens of this specific member of the batch
+                        final_indices.push(j);
+                    }
+                }
+
+                let final_indices_length = final_indices.len();
+                let final_indices =
+                    Tensor::from_vec(final_indices, final_indices_length, &self.device)?;
+
+                // Select the tokens with final indices
+                Some(outputs.index_select(&final_indices, 0)?)
+            } else {
+                Some(outputs)
+            }
+        } else {
+            None
+        };
+
+        Ok((pooled_embeddings, raw_embeddings))
+    }
+}
+
+impl Model for FlashGTEModel {
+    fn is_padded(&self) -> bool {
+        false
+    }
+    fn embed(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        self.forward(batch)
+    }
+}
diff --git a/backends/candle/src/models/flash_jina.rs b/backends/candle/src/models/flash_jina.rs
index ebf2c292..83c5b0a4 100644
--- a/backends/candle/src/models/flash_jina.rs
+++ b/backends/candle/src/models/flash_jina.rs
@@ -1,14 +1,14 @@
 use crate::alibi::alibi_head_slopes;
 use crate::flash_attn::flash_attn_varlen;
 use crate::layers::{HiddenAct, LayerNorm, Linear};
-use crate::models::bert::{BertConfig, PositionEmbeddingType};
-use crate::models::jina::BertEmbeddings;
-use crate::models::Model;
+use crate::models::bert::PositionEmbeddingType;
+use crate::models::jina::JinaEmbeddings;
+use crate::models::{BertConfig, Model};
 use candle::{DType, Device, IndexOp, Result, Tensor};
 use candle_nn::VarBuilder;
 use text_embeddings_backend_core::{Batch, ModelType, Pool};
 
-struct AlibiBertAttention {
+struct JinaAttention {
     qkv_linear: Linear,
     dense: Linear,
     layer_norm: LayerNorm,
@@ -22,7 +22,7 @@ struct AlibiBertAttention {
     span: tracing::Span,
 }
 
-impl AlibiBertAttention {
+impl JinaAttention {
     pub fn load(vb: VarBuilder, config: &BertConfig, alibi_slopes: Option<Tensor>) -> Result<Self> {
         let attention_head_size = config.hidden_size / config.num_attention_heads;
         let all_head_size = config.num_attention_heads * attention_head_size;
@@ -105,6 +105,7 @@ impl AlibiBertAttention {
             max_s,
             self.softmax_scale,
             false,
+            None,
         )?;
         let attention = attention.flatten_from(candle::D::Minus2)?;
 
@@ -116,7 +117,7 @@ impl AlibiBertAttention {
 }
 
 struct JinaBertLayer {
-    attention: AlibiBertAttention,
+    attention: JinaAttention,
     gated: Linear,
     output: Linear,
     layer_norm: LayerNorm,
@@ -129,7 +130,7 @@ struct JinaBertLayer {
 
 impl JinaBertLayer {
     pub fn load(vb: VarBuilder, config: &BertConfig, alibi: Option<Tensor>) -> Result<Self> {
-        let attention = AlibiBertAttention::load(vb.pp("attention"), config, alibi)?;
+        let attention = JinaAttention::load(vb.pp("attention"), config, alibi)?;
 
         let gated_weight = vb
             .pp("mlp")
@@ -173,14 +174,14 @@ impl JinaBertLayer {
         let residual = hidden_states.clone();
 
         let hidden_states = self.gated.forward(&hidden_states)?;
-        let gated = hidden_states.i((.., 0..self.intermediate_size))?;
+        let gated = hidden_states.narrow(1, 0, self.intermediate_size)?;
         let gated = match self.act {
             HiddenAct::Gelu => gated.gelu(),
             HiddenAct::Relu => gated.relu(),
             HiddenAct::Swiglu => gated.silu(),
         }?;
 
-        let non_gated = hidden_states.i((.., self.intermediate_size..))?;
+        let non_gated = hidden_states.narrow(1, self.intermediate_size, self.intermediate_size)?;
         let hidden_states = (gated * non_gated)?;
 
         let hidden_states = self.output.forward(&hidden_states)?;
@@ -190,12 +191,12 @@ impl JinaBertLayer {
     }
 }
 
-struct BertEncoder {
+struct JinaBertEncoder {
     layers: Vec<JinaBertLayer>,
     span: tracing::Span,
 }
 
-impl BertEncoder {
+impl JinaBertEncoder {
     pub fn load(vb: VarBuilder, config: &BertConfig, alibi: Option<Tensor>) -> Result<Self> {
         let layers = (0..config.num_hidden_layers)
             .map(|index| {
@@ -204,7 +205,7 @@ impl BertEncoder {
             .collect::<Result<Vec<_>>>()?;
         let span = tracing::span!(tracing::Level::TRACE, "encoder");
 
-        Ok(BertEncoder { layers, span })
+        Ok(JinaBertEncoder { layers, span })
     }
 
     fn forward(&self, hidden_states: &Tensor, cu_seqlens: &Tensor, max_s: usize) -> Result<Tensor> {
@@ -222,8 +223,8 @@ impl BertEncoder {
 }
 
 pub struct FlashJinaBertModel {
-    embeddings: BertEmbeddings,
-    encoder: BertEncoder,
+    embeddings: JinaEmbeddings,
+    encoder: JinaBertEncoder,
     pool: Pool,
     pub device: Device,
 
@@ -241,6 +242,7 @@ impl FlashJinaBertModel {
                 )
             }
             PositionEmbeddingType::Absolute => None,
+            _ => candle::bail!("not supported"),
         };
 
         match vb.device() {
@@ -265,14 +267,14 @@ impl FlashJinaBertModel {
         };
 
         let (embeddings, encoder) = match (
-            BertEmbeddings::load(vb.pp("embeddings"), config),
-            BertEncoder::load(vb.pp("encoder"), config, alibi.clone()),
+            JinaEmbeddings::load(vb.pp("embeddings"), config),
+            JinaBertEncoder::load(vb.pp("encoder"), config, alibi.clone()),
         ) {
             (Ok(embeddings), Ok(encoder)) => (embeddings, encoder),
             (Err(err), _) | (_, Err(err)) => {
                 if let (Ok(embeddings), Ok(encoder)) = (
-                    BertEmbeddings::load(vb.pp("bert.embeddings"), config),
-                    BertEncoder::load(vb.pp("bert.encoder"), config, alibi.clone()),
+                    JinaEmbeddings::load(vb.pp("bert.embeddings"), config),
+                    JinaBertEncoder::load(vb.pp("bert.encoder"), config, alibi.clone()),
                 ) {
                     (embeddings, encoder)
                 } else {
@@ -319,11 +321,18 @@ impl FlashJinaBertModel {
 
         let pooled_embeddings = if has_pooling_requests {
             match self.pool {
-                // CLS pooling
-                Pool::Cls => {
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
                     if batch_size > 1 {
-                        // Get the indices of the cls tokens from cu_seqlens
-                        let mut cls_indices = cu_seqlens.narrow(0, 0, batch_size)?;
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
 
                         // If raw_indices is empty, we don't need to do anything with
                         // the pooled_indices
@@ -336,13 +345,22 @@ impl FlashJinaBertModel {
                             )?;
 
                             // Only select indices that requires pooling
-                            cls_indices = cls_indices.index_select(&pooled_indices, 0)?
+                            indices = indices.index_select(&pooled_indices, 0)?
                         }
 
-                        // Select cls tokens
-                        Some(outputs.index_select(&cls_indices, 0)?)
+                        // Select tokens
+                        Some(outputs.index_select(&indices, 0)?)
                     } else {
-                        Some(outputs.i(0)?)
+                        Some(
+                            match self.pool {
+                                Pool::Cls => outputs.i(0)?,
+                                Pool::LastToken => {
+                                    outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                                }
+                                _ => unreachable!(),
+                            }
+                            .unsqueeze(0)?,
+                        )
                     }
                 }
                 // Mean pooling
diff --git a/backends/candle/src/models/flash_jina_code.rs b/backends/candle/src/models/flash_jina_code.rs
new file mode 100644
index 00000000..56ab1976
--- /dev/null
+++ b/backends/candle/src/models/flash_jina_code.rs
@@ -0,0 +1,491 @@
+use crate::alibi::alibi_head_slopes;
+use crate::flash_attn::flash_attn_varlen;
+use crate::layers::{HiddenAct, LayerNorm, Linear};
+use crate::models::bert::PositionEmbeddingType;
+use crate::models::jina::JinaEmbeddings;
+use crate::models::{BertConfig, Model};
+use candle::{DType, Device, IndexOp, Result, Tensor};
+use candle_nn::VarBuilder;
+use text_embeddings_backend_core::{Batch, ModelType, Pool};
+
+struct JinaCodeAttention {
+    qkv_linear: Linear,
+
+    dense: Linear,
+    layer_norm_q: LayerNorm,
+    layer_norm_k: LayerNorm,
+    layer_norm_out: LayerNorm,
+
+    alibi_slopes: Option<Tensor>,
+
+    num_attention_heads: usize,
+    attention_head_size: usize,
+    softmax_scale: f32,
+
+    span: tracing::Span,
+}
+
+impl JinaCodeAttention {
+    pub fn load(vb: VarBuilder, config: &BertConfig, alibi_slopes: Option<Tensor>) -> Result<Self> {
+        let attention_head_size = config.hidden_size / config.num_attention_heads;
+        let all_head_size = config.num_attention_heads * attention_head_size;
+        let hidden_size = config.hidden_size;
+
+        let query_weight = vb
+            .pp("self.query")
+            .get((all_head_size, hidden_size), "weight")?;
+        let query_bias = vb.pp("self.query").get(all_head_size, "bias")?;
+        let key_weight = vb
+            .pp("self.key")
+            .get((all_head_size, hidden_size), "weight")?;
+        let key_bias = vb.pp("self.key").get(all_head_size, "bias")?;
+        let value_weight = vb
+            .pp("self.value")
+            .get((all_head_size, hidden_size), "weight")?;
+        let value_bias = vb.pp("self.value").get(all_head_size, "bias")?;
+
+        let qkv_weight = Tensor::cat(&[&query_weight, &key_weight, &value_weight], 0)?;
+        let qkv_bias = Tensor::cat(&[&query_bias, &key_bias, &value_bias], 0)?;
+
+        let qkv_linear = Linear::new(qkv_weight, Some(qkv_bias), None);
+
+        let layer_norm_q = LayerNorm::load(
+            vb.pp("self").pp("layer_norm_q"),
+            config.hidden_size,
+            config.layer_norm_eps as f32,
+        )?;
+        let layer_norm_k = LayerNorm::load(
+            vb.pp("self").pp("layer_norm_k"),
+            config.hidden_size,
+            config.layer_norm_eps as f32,
+        )?;
+
+        let dense_weight = vb
+            .pp("output")
+            .pp("dense")
+            .get((hidden_size, hidden_size), "weight")?;
+        let dense_bias = vb.pp("output").pp("dense").get(hidden_size, "bias")?;
+
+        let dense = Linear::new(dense_weight, Some(dense_bias), None);
+
+        let layer_norm_out = LayerNorm::load(
+            vb.pp("output").pp("LayerNorm"),
+            config.hidden_size,
+            config.layer_norm_eps as f32,
+        )?;
+
+        let softmax_scale = (1. / (attention_head_size as f64).sqrt()) as f32;
+
+        Ok(Self {
+            qkv_linear,
+            dense,
+            layer_norm_q,
+            layer_norm_k,
+            layer_norm_out,
+            alibi_slopes,
+            num_attention_heads: config.num_attention_heads,
+            attention_head_size,
+            softmax_scale,
+            span: tracing::span!(tracing::Level::TRACE, "attention"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        cu_seqlens: &Tensor,
+        max_s: usize,
+    ) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let residual = hidden_states.clone();
+
+        let qkv = self.qkv_linear.forward(hidden_states)?;
+
+        let mut new_qkv_shape = qkv.dims().to_vec();
+        new_qkv_shape.pop();
+        new_qkv_shape.push(self.num_attention_heads * 3);
+        new_qkv_shape.push(self.attention_head_size);
+
+        let qkv = qkv.reshape(new_qkv_shape.as_slice())?;
+        // Splits heads
+        let qkv = qkv.chunk(3, 1)?;
+
+        // Flatten last dims again to go through the layer norm
+        let query_layer = &qkv[0].flatten_from(candle::D::Minus2)?;
+        let key_layer = &qkv[1].flatten_from(candle::D::Minus2)?;
+
+        // Layer norm on q and k
+        let query_layer = self.layer_norm_q.forward(&query_layer, None)?;
+        let key_layer = self.layer_norm_k.forward(&key_layer, None)?;
+
+        // Reshape back
+        let mut new_qk_shape = query_layer.dims().to_vec();
+        new_qk_shape.pop();
+        new_qk_shape.push(self.num_attention_heads);
+        new_qk_shape.push(self.attention_head_size);
+
+        let query_layer = query_layer.reshape(new_qk_shape.as_slice())?;
+        let key_layer = key_layer.reshape(new_qk_shape.as_slice())?;
+
+        let value_layer = &qkv[2];
+
+        let attention = flash_attn_varlen(
+            &query_layer,
+            &key_layer,
+            value_layer,
+            self.alibi_slopes.as_ref(),
+            cu_seqlens,
+            cu_seqlens,
+            max_s,
+            max_s,
+            self.softmax_scale,
+            false,
+            None,
+        )?;
+        let attention = attention.flatten_from(candle::D::Minus2)?;
+
+        let hidden_states = self.dense.forward(&attention)?;
+        let hidden_states = self
+            .layer_norm_out
+            .forward(&hidden_states, Some(&residual))?;
+
+        Ok(hidden_states)
+    }
+}
+
+struct JinaCodeBertLayer {
+    attention: JinaCodeAttention,
+    up_gated_layer: Linear,
+    down_layer: Linear,
+    layer_norm_1: LayerNorm,
+    layer_norm_2: LayerNorm,
+    act: HiddenAct,
+
+    intermediate_size: usize,
+
+    span: tracing::Span,
+}
+
+impl JinaCodeBertLayer {
+    pub fn load(vb: VarBuilder, config: &BertConfig, alibi: Option<Tensor>) -> Result<Self> {
+        let attention = JinaCodeAttention::load(vb.pp("attention"), config, alibi)?;
+
+        let up_gated_weight = vb
+            .pp("mlp")
+            .pp("up_gated_layer")
+            .get((config.intermediate_size * 2, config.hidden_size), "weight")?;
+        let up_gated_layer = Linear::new(up_gated_weight, None, None);
+
+        let down_weight = vb
+            .pp("mlp")
+            .pp("down_layer")
+            .get((config.hidden_size, config.intermediate_size), "weight")?;
+        let down_bias = vb
+            .pp("mlp")
+            .pp("down_layer")
+            .get(config.hidden_size, "bias")?;
+        let down_layer = Linear::new(down_weight, Some(down_bias), None);
+
+        let layer_norm_1 = LayerNorm::load(
+            vb.pp("layer_norm_1"),
+            config.hidden_size,
+            config.layer_norm_eps as f32,
+        )?;
+        let layer_norm_2 = LayerNorm::load(
+            vb.pp("layer_norm_2"),
+            config.hidden_size,
+            config.layer_norm_eps as f32,
+        )?;
+
+        Ok(Self {
+            attention,
+            up_gated_layer,
+            down_layer,
+            layer_norm_1,
+            layer_norm_2,
+            act: config.hidden_act.clone(),
+            intermediate_size: config.intermediate_size,
+            span: tracing::span!(tracing::Level::TRACE, "layer"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        cu_seqlens: &Tensor,
+        max_s: usize,
+    ) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let residual = hidden_states.clone();
+        let hidden_states = self.attention.forward(hidden_states, cu_seqlens, max_s)?;
+
+        // Pre-MLP LayerNorm
+        let hidden_states = self.layer_norm_1.forward(&hidden_states, Some(&residual))?;
+
+        // MLP block
+        let residual = hidden_states.clone();
+        let hidden_states = self.up_gated_layer.forward(&hidden_states)?;
+        let non_gated = hidden_states.narrow(1, 0, self.intermediate_size)?;
+        let gated = hidden_states.narrow(1, self.intermediate_size, self.intermediate_size)?;
+        let gated = match self.act {
+            HiddenAct::Gelu => gated.gelu(),
+            HiddenAct::Relu => gated.relu(),
+            HiddenAct::Swiglu => gated.silu(),
+        }?;
+        let hidden_states = (non_gated * gated)?;
+        let hidden_states = self.down_layer.forward(&hidden_states)?;
+
+        // Post-MLP LayerNorm
+        let hidden_states = self.layer_norm_2.forward(&hidden_states, Some(&residual))?;
+
+        Ok(hidden_states)
+    }
+}
+
+struct JinaCodeBertEncoder {
+    layers: Vec<JinaCodeBertLayer>,
+    span: tracing::Span,
+}
+
+impl JinaCodeBertEncoder {
+    pub fn load(vb: VarBuilder, config: &BertConfig, alibi: Option<Tensor>) -> Result<Self> {
+        let layers = (0..config.num_hidden_layers)
+            .map(|index| {
+                JinaCodeBertLayer::load(vb.pp(format!("layer.{index}")), config, alibi.clone())
+            })
+            .collect::<Result<Vec<_>>>()?;
+        let span = tracing::span!(tracing::Level::TRACE, "encoder");
+
+        Ok(JinaCodeBertEncoder { layers, span })
+    }
+
+    fn forward(&self, hidden_states: &Tensor, cu_seqlens: &Tensor, max_s: usize) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let mut hidden_states = hidden_states.clone();
+
+        // Use a loop rather than a fold as it's easier to modify when adding debug/...
+        for layer in self.layers.iter() {
+            hidden_states = layer.forward(&hidden_states, cu_seqlens, max_s)?
+        }
+
+        Ok(hidden_states)
+    }
+}
+
+pub struct FlashJinaCodeBertModel {
+    embeddings: JinaEmbeddings,
+    encoder: JinaCodeBertEncoder,
+    pool: Pool,
+    pub device: Device,
+
+    span: tracing::Span,
+}
+
+impl FlashJinaCodeBertModel {
+    pub fn load(vb: VarBuilder, config: &BertConfig, model_type: ModelType) -> Result<Self> {
+        let alibi = match config.position_embedding_type {
+            PositionEmbeddingType::Alibi => {
+                let alibi_slopes = alibi_head_slopes(config.num_attention_heads);
+                Some(
+                    Tensor::from_vec(alibi_slopes, config.num_attention_heads, vb.device())?
+                        .to_dtype(DType::F32)?,
+                )
+            }
+            PositionEmbeddingType::Absolute => None,
+            _ => candle::bail!("not supported"),
+        };
+
+        match vb.device() {
+            Device::Cuda(_) => {}
+            _ => candle::bail!("FlashJinaCodeBertModel requires Cuda"),
+        }
+
+        if vb.dtype() != DType::F16 {
+            candle::bail!("FlashJinaCodeBertModel requires DType::F16")
+        }
+
+        let pool = match model_type {
+            ModelType::Classifier => {
+                candle::bail!("`classifier` model type is not supported for Jina Code")
+            }
+            ModelType::Embedding(pool) => {
+                if pool == Pool::Splade {
+                    candle::bail!("`splade` is not supported for Jina Code")
+                }
+                pool
+            }
+        };
+
+        let (embeddings, encoder) = match (
+            JinaEmbeddings::load(vb.pp("embeddings"), config),
+            JinaCodeBertEncoder::load(vb.pp("encoder"), config, alibi.clone()),
+        ) {
+            (Ok(embeddings), Ok(encoder)) => (embeddings, encoder),
+            (Err(err), _) | (_, Err(err)) => {
+                if let (Ok(embeddings), Ok(encoder)) = (
+                    JinaEmbeddings::load(vb.pp("bert.embeddings"), config),
+                    JinaCodeBertEncoder::load(vb.pp("bert.encoder"), config, alibi.clone()),
+                ) {
+                    (embeddings, encoder)
+                } else {
+                    return Err(err);
+                }
+            }
+        };
+
+        Ok(Self {
+            embeddings,
+            encoder,
+            pool,
+            device: vb.device().clone(),
+            span: tracing::span!(tracing::Level::TRACE, "model"),
+        })
+    }
+
+    pub fn forward(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        let _enter = self.span.enter();
+
+        let batch_size = batch.len();
+        let shape = batch.input_ids.len();
+
+        // Create Cuda tensors
+        let input_ids = Tensor::from_vec(batch.input_ids, shape, &self.device)?;
+        let type_ids = Tensor::from_vec(batch.token_type_ids, shape, &self.device)?;
+        let position_ids = Tensor::from_vec(batch.position_ids, shape, &self.device)?;
+        let cu_seqlens = Tensor::from_vec(
+            batch.cumulative_seq_lengths.clone(),
+            batch_size + 1,
+            &self.device,
+        )?;
+
+        let embedding_output = self
+            .embeddings
+            .forward(&input_ids, &type_ids, &position_ids)?;
+
+        let outputs =
+            self.encoder
+                .forward(&embedding_output, &cu_seqlens, batch.max_length as usize)?;
+
+        let has_pooling_requests = !batch.pooled_indices.is_empty();
+        let has_raw_requests = !batch.raw_indices.is_empty();
+
+        let pooled_embeddings = if has_pooling_requests {
+            match self.pool {
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
+                    if batch_size > 1 {
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
+
+                        // If raw_indices is empty, we don't need to do anything with
+                        // the pooled_indices
+                        if has_raw_requests {
+                            // We need the pooled indices to select the correct cls indices
+                            let pooled_indices = Tensor::from_vec(
+                                batch.pooled_indices.clone(),
+                                batch.pooled_indices.len(),
+                                &self.device,
+                            )?;
+
+                            // Only select indices that requires pooling
+                            indices = indices.index_select(&pooled_indices, 0)?
+                        }
+
+                        // Select tokens
+                        Some(outputs.index_select(&indices, 0)?)
+                    } else {
+                        Some(
+                            match self.pool {
+                                Pool::Cls => outputs.i(0)?,
+                                Pool::LastToken => {
+                                    outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                                }
+                                _ => unreachable!(),
+                            }
+                            .unsqueeze(0)?,
+                        )
+                    }
+                }
+                // Mean pooling
+                Pool::Mean => {
+                    if batch_size > 1 {
+                        // for each request that requires pooling
+                        let results: Result<Vec<Tensor>> = batch
+                            .pooled_indices
+                            .into_iter()
+                            .map(|i| {
+                                let i = i as usize;
+                                let start = batch.cumulative_seq_lengths[i];
+                                let len = batch.cumulative_seq_lengths[i + 1] - start;
+
+                                // Mean
+                                let embeddings = outputs.narrow(0, start as usize, len as usize)?;
+                                embeddings.sum_keepdim(0)? / (len as f64)
+                            })
+                            .collect();
+
+                        // Concatenate all results
+                        Some(Tensor::cat(&results?, 0)?)
+                    } else {
+                        Some((outputs.sum_keepdim(0)? / (batch.max_length as f64))?)
+                    }
+                }
+                Pool::Splade => {
+                    unreachable!();
+                }
+            }
+        } else {
+            None
+        };
+
+        let raw_embeddings = if has_raw_requests {
+            if batch_size > 1 && has_pooling_requests {
+                // Create indexing vector for the embeddings
+                let mut final_indices: Vec<u32> = Vec::with_capacity(shape);
+                for i in batch.raw_indices.into_iter() {
+                    let i = i as usize;
+                    // Get start/end token index of this specific member of the batch
+                    let start = batch.cumulative_seq_lengths[i];
+                    let end = batch.cumulative_seq_lengths[i + 1];
+
+                    for j in start..end {
+                        // Add indices for the tokens of this specific member of the batch
+                        final_indices.push(j);
+                    }
+                }
+
+                let final_indices_length = final_indices.len();
+                let final_indices =
+                    Tensor::from_vec(final_indices, final_indices_length, &self.device)?;
+
+                // Select the tokens with final indices
+                Some(outputs.index_select(&final_indices, 0)?)
+            } else {
+                Some(outputs)
+            }
+        } else {
+            None
+        };
+
+        Ok((pooled_embeddings, raw_embeddings))
+    }
+}
+
+impl Model for FlashJinaCodeBertModel {
+    fn is_padded(&self) -> bool {
+        false
+    }
+    fn embed(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        self.forward(batch)
+    }
+}
diff --git a/backends/candle/src/models/flash_mistral.rs b/backends/candle/src/models/flash_mistral.rs
new file mode 100644
index 00000000..eb94c913
--- /dev/null
+++ b/backends/candle/src/models/flash_mistral.rs
@@ -0,0 +1,445 @@
+use crate::flash_attn::flash_attn_varlen;
+use crate::layers::{HiddenAct, Linear, RMSNorm};
+use crate::models::{MistralConfig, Model};
+use candle::{DType, Device, IndexOp, Result, Tensor};
+use candle_nn::{Embedding, Module, VarBuilder};
+use text_embeddings_backend_core::{Batch, ModelType, Pool};
+
+struct MistralAttention {
+    qkv_linear: Linear,
+    o_proj: Linear,
+
+    window_size_left: Option<usize>,
+
+    num_attention_heads: usize,
+    num_key_value_heads: usize,
+    attention_head_size: usize,
+
+    softmax_scale: f32,
+
+    span: tracing::Span,
+}
+
+impl MistralAttention {
+    pub fn load(vb: VarBuilder, config: &MistralConfig) -> Result<Self> {
+        let window_size_left = config.sliding_window;
+        let num_attention_heads = config.num_attention_heads;
+        let attention_head_size = config.hidden_size / config.num_attention_heads;
+        let num_key_value_heads = config.num_key_value_heads;
+        let hidden_size = config.hidden_size;
+
+        let query_weight = vb.pp("q_proj").get((hidden_size, hidden_size), "weight")?;
+
+        let key_weight = vb.pp("k_proj").get(
+            (num_key_value_heads * attention_head_size, hidden_size),
+            "weight",
+        )?;
+
+        let value_weight = vb.pp("v_proj").get(
+            (num_key_value_heads * attention_head_size, hidden_size),
+            "weight",
+        )?;
+
+        let qkv_weight = Tensor::cat(&[&query_weight, &key_weight, &value_weight], 0)?;
+        let qkv_linear = Linear::new(qkv_weight, None, None);
+
+        let o_proj_weight = vb.pp("o_proj").get((hidden_size, hidden_size), "weight")?;
+
+        let o_proj = Linear::new(o_proj_weight, None, None);
+
+        let softmax_scale = (1. / (attention_head_size as f64).sqrt()) as f32;
+
+        Ok(Self {
+            qkv_linear,
+            o_proj,
+            window_size_left,
+            num_attention_heads,
+            num_key_value_heads,
+            attention_head_size,
+            softmax_scale,
+            span: tracing::span!(tracing::Level::TRACE, "attention"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        cu_seqlens: &Tensor,
+        cos: &Tensor,
+        sin: &Tensor,
+        max_s: usize,
+    ) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let qkv = self.qkv_linear.forward(hidden_states)?;
+
+        // Reshape to [tokens, heads, head_size]
+        let mut new_qkv_shape = qkv.dims().to_vec();
+        new_qkv_shape.pop();
+        new_qkv_shape.push(self.num_attention_heads + 2 * self.num_key_value_heads);
+        new_qkv_shape.push(self.attention_head_size);
+
+        let qkv = qkv.reshape(new_qkv_shape)?;
+
+        // Split qkv tensor
+        let q = qkv.narrow(1, 0, self.num_attention_heads)?;
+        let k = qkv.narrow(1, self.num_attention_heads, self.num_key_value_heads)?;
+        let v = qkv.narrow(
+            1,
+            self.num_attention_heads + self.num_key_value_heads,
+            self.num_key_value_heads,
+        )?;
+
+        candle_rotary::apply_rotary_inplace(&q, &k, &cos, &sin, true)?;
+
+        let attention = flash_attn_varlen(
+            &q,
+            &k,
+            &v,
+            None,
+            cu_seqlens,
+            cu_seqlens,
+            max_s,
+            max_s,
+            self.softmax_scale,
+            true,
+            self.window_size_left,
+        )?;
+        let attention = attention.flatten_from(candle::D::Minus2)?;
+
+        self.o_proj.forward(&attention)
+    }
+}
+
+struct MistralMLP {
+    gate_up_proj: Linear,
+    down_proj: Linear,
+
+    act: HiddenAct,
+    intermediate_size: usize,
+
+    span: tracing::Span,
+}
+
+impl MistralMLP {
+    pub fn load(vb: VarBuilder, config: &MistralConfig) -> Result<Self> {
+        let intermediate_size = config.intermediate_size;
+
+        let gate_proj_weight = vb
+            .pp("gate_proj")
+            .get((intermediate_size, config.hidden_size), "weight")?;
+
+        let up_proj_weight = vb
+            .pp("up_proj")
+            .get((intermediate_size, config.hidden_size), "weight")?;
+
+        let gate_up_proj_weight = Tensor::cat(&[&gate_proj_weight, &up_proj_weight], 0)?;
+        let gate_up_proj = Linear::new(gate_up_proj_weight, None, None);
+
+        let down_proj_weight = vb
+            .pp("down_proj")
+            .get((config.hidden_size, intermediate_size), "weight")?;
+        let down_proj = Linear::new(down_proj_weight, None, None);
+
+        Ok(Self {
+            gate_up_proj,
+            down_proj,
+            intermediate_size,
+            act: config.hidden_act.clone(),
+            span: tracing::span!(tracing::Level::TRACE, "mlp"),
+        })
+    }
+
+    pub fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let gate_up_states = self.gate_up_proj.forward(hidden_states)?;
+        let gate_states = gate_up_states.narrow(1, 0, self.intermediate_size)?;
+        let up_states = gate_up_states.narrow(1, self.intermediate_size, self.intermediate_size)?;
+
+        let gate_states = match self.act {
+            HiddenAct::Gelu => gate_states.gelu(),
+            HiddenAct::Relu => gate_states.relu(),
+            HiddenAct::Swiglu => gate_states.silu(),
+        }?;
+        let r = self.down_proj.forward(&(gate_states * up_states)?);
+        r
+    }
+}
+
+struct MistralLayer {
+    attention: MistralAttention,
+    mlp: MistralMLP,
+    input_layer_norm: RMSNorm,
+    post_attention_layer_norm: RMSNorm,
+
+    span: tracing::Span,
+}
+
+impl MistralLayer {
+    pub fn load(vb: VarBuilder, config: &MistralConfig) -> Result<Self> {
+        let attention = MistralAttention::load(vb.pp("self_attn"), config)?;
+        let mlp = MistralMLP::load(vb.pp("mlp"), config)?;
+
+        let input_layer_norm = RMSNorm::load(
+            vb.pp("input_layernorm"),
+            config.hidden_size,
+            config.rms_norm_eps,
+        )?;
+        let post_attention_layer_norm = RMSNorm::load(
+            vb.pp("post_attention_layernorm"),
+            config.hidden_size,
+            config.rms_norm_eps,
+        )?;
+
+        Ok(Self {
+            attention,
+            mlp,
+            input_layer_norm,
+            post_attention_layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "layer"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        residual: Option<&Tensor>,
+        cu_seqlens: &Tensor,
+        cos: &Tensor,
+        sin: &Tensor,
+        max_s: usize,
+    ) -> Result<(Tensor, Tensor)> {
+        let _enter = self.span.enter();
+
+        let (normed_hidden_states, res) = self.input_layer_norm.forward(hidden_states, residual)?;
+        let attn_output =
+            self.attention
+                .forward(&normed_hidden_states, cu_seqlens, cos, sin, max_s)?;
+        let (normed_attn_res_output, attn_res) = self
+            .post_attention_layer_norm
+            .forward(&attn_output, Some(&res))?;
+        let mlp_output = self.mlp.forward(&normed_attn_res_output)?;
+
+        Ok((mlp_output, attn_res))
+    }
+}
+
+pub struct FlashMistralModel {
+    embeddings: Embedding,
+    layers: Vec<MistralLayer>,
+    norm: RMSNorm,
+    cos_cache: Tensor,
+    sin_cache: Tensor,
+    pool: Pool,
+    pub device: Device,
+
+    span: tracing::Span,
+}
+
+impl FlashMistralModel {
+    pub fn load(vb: VarBuilder, config: &MistralConfig, model_type: ModelType) -> Result<Self> {
+        match vb.device() {
+            Device::Cuda(_) => {}
+            _ => candle::bail!("FlashMistral requires Cuda"),
+        }
+
+        if vb.dtype() != DType::F16 {
+            candle::bail!("FlashMistral requires DType::F16")
+        }
+
+        let pool = match model_type {
+            ModelType::Classifier => {
+                candle::bail!("`classifier` model type is not supported for Mistral")
+            }
+            ModelType::Embedding(pool) => pool,
+        };
+
+        let embeddings = Embedding::new(
+            vb.pp("embed_tokens")
+                .get((config.vocab_size, config.hidden_size), "weight")?,
+            config.hidden_size,
+        );
+
+        let layers = (0..config.num_hidden_layers)
+            .map(|index| MistralLayer::load(vb.pp(format!("layers.{index}")), config))
+            .collect::<Result<Vec<_>>>()?;
+
+        let norm = RMSNorm::load(vb.pp("norm"), config.hidden_size, config.rms_norm_eps)?;
+
+        let inv_freqs = candle_rotary::inv_freqs(
+            layers[0].attention.attention_head_size,
+            config.rope_theta,
+            vb.device(),
+        )?;
+        let (cos_cache, sin_cache) =
+            candle_rotary::cos_sin(config.max_position_embeddings, &inv_freqs, vb.dtype())?;
+
+        Ok(Self {
+            embeddings,
+            layers,
+            norm,
+            cos_cache,
+            sin_cache,
+            pool,
+            device: vb.device().clone(),
+            span: tracing::span!(tracing::Level::TRACE, "model"),
+        })
+    }
+
+    pub fn forward(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        let _enter = self.span.enter();
+
+        let batch_size = batch.cumulative_seq_lengths.len() - 1;
+        let shape = batch.input_ids.len();
+
+        // Create Cuda tensors
+        let input_ids = Tensor::from_vec(batch.input_ids, shape, &self.device)?;
+        let position_ids = Tensor::from_vec(batch.position_ids, shape, &self.device)?;
+        let cu_seqlens = Tensor::from_vec(
+            batch.cumulative_seq_lengths.clone(),
+            batch_size + 1,
+            &self.device,
+        )?;
+
+        let mut hidden_states = self.embeddings.forward(&input_ids)?;
+
+        let cos = self.cos_cache.index_select(&position_ids, 0)?;
+        let sin = self.sin_cache.index_select(&position_ids, 0)?;
+
+        let mut residual = None;
+        for layer in &self.layers {
+            let (h, r) = layer.forward(
+                &hidden_states,
+                residual.as_ref(),
+                &cu_seqlens,
+                &cos,
+                &sin,
+                batch.max_length as usize,
+            )?;
+            hidden_states = h;
+            residual = Some(r);
+        }
+
+        let (outputs, _) = self.norm.forward(&hidden_states, residual.as_ref())?;
+
+        let has_pooling_requests = !batch.pooled_indices.is_empty();
+        let has_raw_requests = !batch.raw_indices.is_empty();
+
+        let pooled_embeddings = if has_pooling_requests {
+            match self.pool {
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
+                    if batch_size > 1 {
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
+
+                        // If raw_indices is empty, we don't need to do anything with
+                        // the pooled_indices
+                        if has_raw_requests {
+                            // We need the pooled indices to select the correct cls indices
+                            let pooled_indices = Tensor::from_vec(
+                                batch.pooled_indices.clone(),
+                                batch.pooled_indices.len(),
+                                &self.device,
+                            )?;
+
+                            // Only select indices that requires pooling
+                            indices = indices.index_select(&pooled_indices, 0)?
+                        }
+
+                        // Select tokens
+                        Some(outputs.index_select(&indices, 0)?)
+                    } else {
+                        Some(
+                            match self.pool {
+                                Pool::Cls => outputs.i(0)?,
+                                Pool::LastToken => {
+                                    outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                                }
+                                _ => unreachable!(),
+                            }
+                            .unsqueeze(0)?,
+                        )
+                    }
+                }
+                // Mean pooling
+                Pool::Mean => {
+                    if batch_size > 1 {
+                        // for each request that requires pooling
+                        let results: Result<Vec<Tensor>> = batch
+                            .pooled_indices
+                            .into_iter()
+                            .map(|i| {
+                                let i = i as usize;
+                                let start = batch.cumulative_seq_lengths[i];
+                                let len = batch.cumulative_seq_lengths[i + 1] - start;
+
+                                // Mean
+                                let embeddings = outputs.narrow(0, start as usize, len as usize)?;
+                                embeddings.sum_keepdim(0)? / (len as f64)
+                            })
+                            .collect();
+
+                        // Concatenate all results
+                        Some(Tensor::cat(&results?, 0)?)
+                    } else {
+                        Some((outputs.sum_keepdim(0)? / (batch.max_length as f64))?)
+                    }
+                }
+                Pool::Splade => {
+                    unreachable!();
+                }
+            }
+        } else {
+            None
+        };
+
+        let raw_embeddings = if has_raw_requests {
+            if batch_size > 1 && has_pooling_requests {
+                // Create indexing vector for the embeddings
+                let mut final_indices: Vec<u32> = Vec::with_capacity(shape);
+                for i in batch.raw_indices.into_iter() {
+                    let i = i as usize;
+                    // Get start/end token index of this specific member of the batch
+                    let start = batch.cumulative_seq_lengths[i];
+                    let end = batch.cumulative_seq_lengths[i + 1];
+
+                    for j in start..end {
+                        // Add indices for the tokens of this specific member of the batch
+                        final_indices.push(j);
+                    }
+                }
+
+                let final_indices_length = final_indices.len();
+                let final_indices =
+                    Tensor::from_vec(final_indices, final_indices_length, &self.device)?;
+
+                // Select the tokens with final indices
+                Some(outputs.index_select(&final_indices, 0)?)
+            } else {
+                Some(outputs)
+            }
+        } else {
+            None
+        };
+
+        Ok((pooled_embeddings, raw_embeddings))
+    }
+}
+
+impl Model for FlashMistralModel {
+    fn is_padded(&self) -> bool {
+        false
+    }
+    fn embed(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        self.forward(batch)
+    }
+}
diff --git a/backends/candle/src/models/flash_nomic.rs b/backends/candle/src/models/flash_nomic.rs
index 12eff0f1..7f558b9e 100644
--- a/backends/candle/src/models/flash_nomic.rs
+++ b/backends/candle/src/models/flash_nomic.rs
@@ -81,6 +81,7 @@ impl NomicAttention {
             max_s,
             self.softmax_scale,
             false,
+            None,
         )?;
         let attention = attention.flatten_from(D::Minus2)?;
 
@@ -304,11 +305,18 @@ impl FlashNomicBertModel {
 
         let pooled_embeddings = if has_pooling_requests {
             match self.pool {
-                // CLS pooling
-                Pool::Cls => {
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
                     if batch_size > 1 {
-                        // Get the indices of the cls tokens from cu_seqlens
-                        let mut cls_indices = cu_seqlens.narrow(0, 0, batch_size)?;
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
 
                         // If raw_indices is empty, we don't need to do anything with
                         // the pooled_indices
@@ -321,13 +329,22 @@ impl FlashNomicBertModel {
                             )?;
 
                             // Only select indices that requires pooling
-                            cls_indices = cls_indices.index_select(&pooled_indices, 0)?
+                            indices = indices.index_select(&pooled_indices, 0)?
                         }
 
-                        // Select cls tokens
-                        Some(outputs.index_select(&cls_indices, 0)?)
+                        // Select tokens
+                        Some(outputs.index_select(&indices, 0)?)
                     } else {
-                        Some(outputs.i(0)?)
+                        Some(
+                            match self.pool {
+                                Pool::Cls => outputs.i(0)?,
+                                Pool::LastToken => {
+                                    outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                                }
+                                _ => unreachable!(),
+                            }
+                            .unsqueeze(0)?,
+                        )
                     }
                 }
                 // Mean pooling
diff --git a/backends/candle/src/models/flash_qwen2.rs b/backends/candle/src/models/flash_qwen2.rs
new file mode 100644
index 00000000..4dba7868
--- /dev/null
+++ b/backends/candle/src/models/flash_qwen2.rs
@@ -0,0 +1,455 @@
+use crate::flash_attn::flash_attn_varlen;
+use crate::layers::{HiddenAct, Linear, RMSNorm};
+use crate::models::{Model, Qwen2Config};
+use candle::{DType, Device, IndexOp, Result, Tensor};
+use candle_nn::{Embedding, Module, VarBuilder};
+use text_embeddings_backend_core::{Batch, ModelType, Pool};
+
+struct Qwen2Attention {
+    qkv_linear: Linear,
+    o_proj: Linear,
+
+    num_attention_heads: usize,
+    num_key_value_heads: usize,
+    attention_head_size: usize,
+
+    softmax_scale: f32,
+
+    span: tracing::Span,
+}
+
+impl Qwen2Attention {
+    pub fn load(vb: VarBuilder, config: &Qwen2Config) -> Result<Self> {
+        if config.use_sliding_window {
+            candle::bail!("Sliding window is not supported");
+        }
+
+        let num_attention_heads = config.num_attention_heads;
+        let attention_head_size = config.hidden_size / config.num_attention_heads;
+        let num_key_value_heads = config.num_key_value_heads;
+        let hidden_size = config.hidden_size;
+
+        let query_weight = vb.pp("q_proj").get((hidden_size, hidden_size), "weight")?;
+        let query_bias = vb.pp("q_proj").get(hidden_size, "bias")?;
+
+        let key_weight = vb.pp("k_proj").get(
+            (num_key_value_heads * attention_head_size, hidden_size),
+            "weight",
+        )?;
+        let key_bias = vb
+            .pp("k_proj")
+            .get(num_key_value_heads * attention_head_size, "bias")?;
+
+        let value_weight = vb.pp("v_proj").get(
+            (num_key_value_heads * attention_head_size, hidden_size),
+            "weight",
+        )?;
+        let value_bias = vb
+            .pp("v_proj")
+            .get(num_key_value_heads * attention_head_size, "bias")?;
+
+        let qkv_weight = Tensor::cat(&[&query_weight, &key_weight, &value_weight], 0)?;
+        let qkv_bias = Tensor::cat(&[&query_bias, &key_bias, &value_bias], 0)?;
+        let qkv_linear = Linear::new(qkv_weight, Some(qkv_bias), None);
+
+        let o_proj_weight = vb.pp("o_proj").get((hidden_size, hidden_size), "weight")?;
+
+        let o_proj = Linear::new(o_proj_weight, None, None);
+
+        let softmax_scale = (1. / (attention_head_size as f64).sqrt()) as f32;
+
+        Ok(Self {
+            qkv_linear,
+            o_proj,
+            num_attention_heads,
+            num_key_value_heads,
+            attention_head_size,
+            softmax_scale,
+            span: tracing::span!(tracing::Level::TRACE, "attention"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        cu_seqlens: &Tensor,
+        cos: &Tensor,
+        sin: &Tensor,
+        max_s: usize,
+    ) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let qkv = self.qkv_linear.forward(hidden_states)?;
+
+        // Reshape to [tokens, heads, head_size]
+        let mut new_qkv_shape = qkv.dims().to_vec();
+        new_qkv_shape.pop();
+        new_qkv_shape.push(self.num_attention_heads + 2 * self.num_key_value_heads);
+        new_qkv_shape.push(self.attention_head_size);
+
+        let qkv = qkv.reshape(new_qkv_shape)?;
+
+        // Split qkv tensor
+        let q = qkv.narrow(1, 0, self.num_attention_heads)?;
+        let k = qkv.narrow(1, self.num_attention_heads, self.num_key_value_heads)?;
+        let v = qkv.narrow(
+            1,
+            self.num_attention_heads + self.num_key_value_heads,
+            self.num_key_value_heads,
+        )?;
+
+        candle_rotary::apply_rotary_inplace(&q, &k, &cos, &sin, true)?;
+
+        let attention = flash_attn_varlen(
+            &q,
+            &k,
+            &v,
+            None,
+            cu_seqlens,
+            cu_seqlens,
+            max_s,
+            max_s,
+            self.softmax_scale,
+            false,
+            None,
+        )?;
+        let attention = attention.flatten_from(candle::D::Minus2)?;
+
+        self.o_proj.forward(&attention)
+    }
+}
+
+struct Qwen2MLP {
+    gate_up_proj: Linear,
+    down_proj: Linear,
+
+    act: HiddenAct,
+    intermediate_size: usize,
+
+    span: tracing::Span,
+}
+
+impl Qwen2MLP {
+    pub fn load(vb: VarBuilder, config: &Qwen2Config) -> Result<Self> {
+        let intermediate_size = config.intermediate_size;
+
+        let gate_proj_weight = vb
+            .pp("gate_proj")
+            .get((intermediate_size, config.hidden_size), "weight")?;
+
+        let up_proj_weight = vb
+            .pp("up_proj")
+            .get((intermediate_size, config.hidden_size), "weight")?;
+
+        let gate_up_proj_weight = Tensor::cat(&[&gate_proj_weight, &up_proj_weight], 0)?;
+        let gate_up_proj = Linear::new(gate_up_proj_weight, None, None);
+
+        let down_proj_weight = vb
+            .pp("down_proj")
+            .get((config.hidden_size, intermediate_size), "weight")?;
+        let down_proj = Linear::new(down_proj_weight, None, None);
+
+        Ok(Self {
+            gate_up_proj,
+            down_proj,
+            intermediate_size,
+            act: config.hidden_act.clone(),
+            span: tracing::span!(tracing::Level::TRACE, "mlp"),
+        })
+    }
+
+    pub fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let gate_up_states = self.gate_up_proj.forward(hidden_states)?;
+        let gate_states = gate_up_states.narrow(1, 0, self.intermediate_size)?;
+        let up_states = gate_up_states.narrow(1, self.intermediate_size, self.intermediate_size)?;
+
+        let gate_states = match self.act {
+            HiddenAct::Gelu => gate_states.gelu(),
+            HiddenAct::Relu => gate_states.relu(),
+            HiddenAct::Swiglu => gate_states.silu(),
+        }?;
+        let r = self.down_proj.forward(&(gate_states * up_states)?);
+        r
+    }
+}
+
+struct Qwen2Layer {
+    attention: Qwen2Attention,
+    mlp: Qwen2MLP,
+    input_layer_norm: RMSNorm,
+    post_attention_layer_norm: RMSNorm,
+
+    span: tracing::Span,
+}
+
+impl Qwen2Layer {
+    pub fn load(vb: VarBuilder, config: &Qwen2Config) -> Result<Self> {
+        let attention = Qwen2Attention::load(vb.pp("self_attn"), config)?;
+        let mlp = Qwen2MLP::load(vb.pp("mlp"), config)?;
+
+        let input_layer_norm = RMSNorm::load(
+            vb.pp("input_layernorm"),
+            config.hidden_size,
+            config.rms_norm_eps,
+        )?;
+        let post_attention_layer_norm = RMSNorm::load(
+            vb.pp("post_attention_layernorm"),
+            config.hidden_size,
+            config.rms_norm_eps,
+        )?;
+
+        Ok(Self {
+            attention,
+            mlp,
+            input_layer_norm,
+            post_attention_layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "layer"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        residual: Option<&Tensor>,
+        cu_seqlens: &Tensor,
+        cos: &Tensor,
+        sin: &Tensor,
+        max_s: usize,
+    ) -> Result<(Tensor, Tensor)> {
+        let _enter = self.span.enter();
+
+        let (normed_hidden_states, res) = self.input_layer_norm.forward(hidden_states, residual)?;
+        let attn_output =
+            self.attention
+                .forward(&normed_hidden_states, cu_seqlens, cos, sin, max_s)?;
+        let (normed_attn_res_output, attn_res) = self
+            .post_attention_layer_norm
+            .forward(&attn_output, Some(&res))?;
+        let mlp_output = self.mlp.forward(&normed_attn_res_output)?;
+
+        Ok((mlp_output, attn_res))
+    }
+}
+
+pub struct FlashQwen2Model {
+    embeddings: Embedding,
+    layers: Vec<Qwen2Layer>,
+    norm: RMSNorm,
+    cos_cache: Tensor,
+    sin_cache: Tensor,
+    pool: Pool,
+    pub device: Device,
+
+    span: tracing::Span,
+}
+
+impl FlashQwen2Model {
+    pub fn load(vb: VarBuilder, config: &Qwen2Config, model_type: ModelType) -> Result<Self> {
+        match vb.device() {
+            Device::Cuda(_) => {}
+            _ => candle::bail!("FlashQwen2 requires Cuda"),
+        }
+
+        if vb.dtype() != DType::F16 {
+            candle::bail!("FlashQwen2 requires DType::F16")
+        }
+
+        let pool = match model_type {
+            ModelType::Classifier => {
+                candle::bail!("`classifier` model type is not supported for Qwen2")
+            }
+            ModelType::Embedding(pool) => pool,
+        };
+
+        let vb = vb.pp("model");
+
+        let embeddings = Embedding::new(
+            vb.pp("embed_tokens")
+                .get((config.vocab_size, config.hidden_size), "weight")?,
+            config.hidden_size,
+        );
+
+        let layers = (0..config.num_hidden_layers)
+            .map(|index| Qwen2Layer::load(vb.pp(format!("layers.{index}")), config))
+            .collect::<Result<Vec<_>>>()?;
+
+        let norm = RMSNorm::load(vb.pp("norm"), config.hidden_size, config.rms_norm_eps)?;
+
+        let inv_freqs = candle_rotary::inv_freqs(
+            layers[0].attention.attention_head_size,
+            config.rope_theta,
+            vb.device(),
+        )?;
+        let (cos_cache, sin_cache) =
+            candle_rotary::cos_sin(config.max_position_embeddings, &inv_freqs, vb.dtype())?;
+
+        Ok(Self {
+            embeddings,
+            layers,
+            norm,
+            cos_cache,
+            sin_cache,
+            pool,
+            device: vb.device().clone(),
+            span: tracing::span!(tracing::Level::TRACE, "model"),
+        })
+    }
+
+    pub fn forward(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        let _enter = self.span.enter();
+
+        let batch_size = batch.cumulative_seq_lengths.len() - 1;
+        let shape = batch.input_ids.len();
+
+        // Create Cuda tensors
+        let input_ids = Tensor::from_vec(batch.input_ids, shape, &self.device)?;
+        let position_ids = Tensor::from_vec(batch.position_ids, shape, &self.device)?;
+        let cu_seqlens = Tensor::from_vec(
+            batch.cumulative_seq_lengths.clone(),
+            batch_size + 1,
+            &self.device,
+        )?;
+
+        let mut hidden_states = self.embeddings.forward(&input_ids)?;
+
+        let cos = self.cos_cache.index_select(&position_ids, 0)?;
+        let sin = self.sin_cache.index_select(&position_ids, 0)?;
+
+        let mut residual = None;
+        for layer in &self.layers {
+            let (h, r) = layer.forward(
+                &hidden_states,
+                residual.as_ref(),
+                &cu_seqlens,
+                &cos,
+                &sin,
+                batch.max_length as usize,
+            )?;
+            hidden_states = h;
+            residual = Some(r);
+        }
+
+        let (outputs, _) = self.norm.forward(&hidden_states, residual.as_ref())?;
+
+        let has_pooling_requests = !batch.pooled_indices.is_empty();
+        let has_raw_requests = !batch.raw_indices.is_empty();
+
+        let pooled_embeddings = if has_pooling_requests {
+            match self.pool {
+                // CLS and LastToken pooling
+                Pool::Cls | Pool::LastToken => {
+                    if batch_size > 1 {
+                        // Get token indices form cu_seqlens
+                        let mut indices = match self.pool {
+                            Pool::Cls => cu_seqlens.narrow(0, 0, batch_size)?,
+                            Pool::LastToken => {
+                                let end = cu_seqlens.narrow(0, 1, batch_size)?;
+                                (&end - &end.ones_like()?)?
+                            }
+                            _ => unreachable!(),
+                        };
+
+                        // If raw_indices is empty, we don't need to do anything with
+                        // the pooled_indices
+                        if has_raw_requests {
+                            // We need the pooled indices to select the correct cls indices
+                            let pooled_indices = Tensor::from_vec(
+                                batch.pooled_indices.clone(),
+                                batch.pooled_indices.len(),
+                                &self.device,
+                            )?;
+
+                            // Only select indices that requires pooling
+                            indices = indices.index_select(&pooled_indices, 0)?
+                        }
+
+                        // Select tokens
+                        Some(outputs.index_select(&indices, 0)?)
+                    } else {
+                        Some(
+                            match self.pool {
+                                Pool::Cls => outputs.i(0)?,
+                                Pool::LastToken => {
+                                    outputs.i(batch.cumulative_seq_lengths[1] as usize - 1)?
+                                }
+                                _ => unreachable!(),
+                            }
+                            .unsqueeze(0)?,
+                        )
+                    }
+                }
+                // Mean pooling
+                Pool::Mean => {
+                    if batch_size > 1 {
+                        // for each request that requires pooling
+                        let results: Result<Vec<Tensor>> = batch
+                            .pooled_indices
+                            .into_iter()
+                            .map(|i| {
+                                let i = i as usize;
+                                let start = batch.cumulative_seq_lengths[i];
+                                let len = batch.cumulative_seq_lengths[i + 1] - start;
+
+                                // Mean
+                                let embeddings = outputs.narrow(0, start as usize, len as usize)?;
+                                embeddings.sum_keepdim(0)? / (len as f64)
+                            })
+                            .collect();
+
+                        // Concatenate all results
+                        Some(Tensor::cat(&results?, 0)?)
+                    } else {
+                        Some((outputs.sum_keepdim(0)? / (batch.max_length as f64))?)
+                    }
+                }
+                Pool::Splade => {
+                    unreachable!();
+                }
+            }
+        } else {
+            None
+        };
+
+        let raw_embeddings = if has_raw_requests {
+            if batch_size > 1 && has_pooling_requests {
+                // Create indexing vector for the embeddings
+                let mut final_indices: Vec<u32> = Vec::with_capacity(shape);
+                for i in batch.raw_indices.into_iter() {
+                    let i = i as usize;
+                    // Get start/end token index of this specific member of the batch
+                    let start = batch.cumulative_seq_lengths[i];
+                    let end = batch.cumulative_seq_lengths[i + 1];
+
+                    for j in start..end {
+                        // Add indices for the tokens of this specific member of the batch
+                        final_indices.push(j);
+                    }
+                }
+
+                let final_indices_length = final_indices.len();
+                let final_indices =
+                    Tensor::from_vec(final_indices, final_indices_length, &self.device)?;
+
+                // Select the tokens with final indices
+                Some(outputs.index_select(&final_indices, 0)?)
+            } else {
+                Some(outputs)
+            }
+        } else {
+            None
+        };
+
+        Ok((pooled_embeddings, raw_embeddings))
+    }
+}
+
+impl Model for FlashQwen2Model {
+    fn is_padded(&self) -> bool {
+        false
+    }
+    fn embed(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        self.forward(batch)
+    }
+}
diff --git a/backends/candle/src/models/gte.rs b/backends/candle/src/models/gte.rs
new file mode 100644
index 00000000..e5e75638
--- /dev/null
+++ b/backends/candle/src/models/gte.rs
@@ -0,0 +1,35 @@
+use crate::layers::HiddenAct;
+use crate::models::PositionEmbeddingType;
+use serde::Deserialize;
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct NTKScaling {
+    pub factor: f32,
+}
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+#[serde(tag = "type", rename_all = "kebab-case")]
+pub enum RopeScaling {
+    Ntk(NTKScaling),
+}
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct GTEConfig {
+    pub vocab_size: usize,
+    pub hidden_size: usize,
+    pub num_hidden_layers: usize,
+    pub num_attention_heads: usize,
+    pub intermediate_size: usize,
+    pub hidden_act: HiddenAct,
+    pub max_position_embeddings: usize,
+    pub type_vocab_size: usize,
+    pub layer_norm_type: String,
+    pub layer_norm_eps: f32,
+    pub position_embedding_type: PositionEmbeddingType,
+    pub rope_theta: f32,
+    pub rope_scaling: Option<RopeScaling>,
+    #[serde(default)]
+    pub logn_attention_scale: bool,
+    #[serde(default)]
+    pub logn_attention_clip1: bool,
+}
diff --git a/backends/candle/src/models/jina.rs b/backends/candle/src/models/jina.rs
index 97bc7f96..ecee8bfe 100644
--- a/backends/candle/src/models/jina.rs
+++ b/backends/candle/src/models/jina.rs
@@ -1,13 +1,13 @@
 use crate::alibi::build_alibi_tensor;
 use crate::layers::{get_cublas_lt_wrapper, HiddenAct, LayerNorm, Linear};
-use crate::models::Model;
-use crate::models::{BertConfig, PositionEmbeddingType};
+use crate::models::PositionEmbeddingType;
+use crate::models::{BertConfig, Model};
 use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
 use candle_nn::{Embedding, VarBuilder};
 use text_embeddings_backend_core::{Batch, ModelType, Pool};
 
 #[derive(Debug)]
-pub struct BertEmbeddings {
+pub struct JinaEmbeddings {
     word_embeddings: Embedding,
     token_type_embeddings: Embedding,
     position_embeddings: Option<Embedding>,
@@ -15,7 +15,7 @@ pub struct BertEmbeddings {
     span: tracing::Span,
 }
 
-impl BertEmbeddings {
+impl JinaEmbeddings {
     pub fn load(vb: VarBuilder, config: &BertConfig) -> Result<Self> {
         let position_embeddings =
             if config.position_embedding_type == PositionEmbeddingType::Absolute {
@@ -74,7 +74,7 @@ impl BertEmbeddings {
     }
 }
 
-struct BertAttention {
+struct JinaAttention {
     qkv_linear: Linear,
 
     dense: Linear,
@@ -87,7 +87,7 @@ struct BertAttention {
     span: tracing::Span,
 }
 
-impl BertAttention {
+impl JinaAttention {
     pub fn load(vb: VarBuilder, config: &BertConfig) -> Result<Self> {
         let attention_head_size = config.hidden_size / config.num_attention_heads;
         let all_head_size = config.num_attention_heads * attention_head_size;
@@ -237,7 +237,7 @@ impl BertAttention {
 }
 
 struct JinaBertLayer {
-    attention: BertAttention,
+    attention: JinaAttention,
     gated: Linear,
     output: Linear,
     layer_norm: LayerNorm,
@@ -250,7 +250,7 @@ struct JinaBertLayer {
 
 impl JinaBertLayer {
     pub fn load(vb: VarBuilder, config: &BertConfig) -> Result<Self> {
-        let attention = BertAttention::load(vb.pp("attention"), config)?;
+        let attention = JinaAttention::load(vb.pp("attention"), config)?;
 
         let gated_weight = vb
             .pp("mlp")
@@ -310,19 +310,19 @@ impl JinaBertLayer {
     }
 }
 
-struct BertEncoder {
+struct JinaBertEncoder {
     layers: Vec<JinaBertLayer>,
     span: tracing::Span,
 }
 
-impl BertEncoder {
+impl JinaBertEncoder {
     pub fn load(vb: VarBuilder, config: &BertConfig) -> Result<Self> {
         let layers = (0..config.num_hidden_layers)
             .map(|index| JinaBertLayer::load(vb.pp(format!("layer.{index}")), config))
             .collect::<Result<Vec<_>>>()?;
         let span = tracing::span!(tracing::Level::TRACE, "encoder");
 
-        Ok(BertEncoder { layers, span })
+        Ok(JinaBertEncoder { layers, span })
     }
 
     fn forward(&self, hidden_states: &Tensor, attention_bias: Option<&Tensor>) -> Result<Tensor> {
@@ -340,8 +340,8 @@ impl BertEncoder {
 }
 
 pub struct JinaBertModel {
-    embeddings: BertEmbeddings,
-    encoder: BertEncoder,
+    embeddings: JinaEmbeddings,
+    encoder: JinaBertEncoder,
     pool: Pool,
     alibi: Option<Tensor>,
 
@@ -363,6 +363,7 @@ impl JinaBertModel {
                 vb.dtype(),
             )?),
             PositionEmbeddingType::Absolute => None,
+            _ => candle::bail!("not supported"),
         };
 
         let pool = match model_type {
@@ -373,19 +374,22 @@ impl JinaBertModel {
                 if pool == Pool::Splade {
                     candle::bail!("`splade` is not supported for Jina")
                 }
+                if pool == Pool::LastToken {
+                    candle::bail!("`last_token` is not supported for Jina");
+                }
                 pool
             }
         };
 
         let (embeddings, encoder) = match (
-            BertEmbeddings::load(vb.pp("embeddings"), config),
-            BertEncoder::load(vb.pp("encoder"), config),
+            JinaEmbeddings::load(vb.pp("embeddings"), config),
+            JinaBertEncoder::load(vb.pp("encoder"), config),
         ) {
             (Ok(embeddings), Ok(encoder)) => (embeddings, encoder),
             (Err(err), _) | (_, Err(err)) => {
                 if let (Ok(embeddings), Ok(encoder)) = (
-                    BertEmbeddings::load(vb.pp("bert.embeddings"), config),
-                    BertEncoder::load(vb.pp("bert.encoder"), config),
+                    JinaEmbeddings::load(vb.pp("bert.embeddings"), config),
+                    JinaBertEncoder::load(vb.pp("bert.encoder"), config),
                 ) {
                     (embeddings, encoder)
                 } else {
@@ -594,6 +598,8 @@ impl JinaBertModel {
             let pooled_embeddings = match self.pool {
                 // CLS pooling
                 Pool::Cls => outputs.i((.., 0))?,
+                // Last token pooling is not supported for this model
+                Pool::LastToken => unreachable!(),
                 // Mean pooling
                 Pool::Mean => {
                     if let Some(ref attention_mask) = attention_mask {
diff --git a/backends/candle/src/models/jina_code.rs b/backends/candle/src/models/jina_code.rs
new file mode 100644
index 00000000..5f13fe08
--- /dev/null
+++ b/backends/candle/src/models/jina_code.rs
@@ -0,0 +1,662 @@
+use crate::alibi::build_alibi_tensor;
+use crate::layers::{get_cublas_lt_wrapper, HiddenAct, LayerNorm, Linear};
+use crate::models::jina::JinaEmbeddings;
+use crate::models::PositionEmbeddingType;
+use crate::models::{BertConfig, Model};
+use candle::{DType, Device, IndexOp, Result, Tensor, D};
+use candle_nn::VarBuilder;
+use text_embeddings_backend_core::{Batch, ModelType, Pool};
+
+struct JinaCodeAttention {
+    qkv_linear: Linear,
+
+    dense: Linear,
+    layer_norm_q: LayerNorm,
+    layer_norm_k: LayerNorm,
+    layer_norm_out: LayerNorm,
+
+    num_attention_heads: usize,
+    attention_head_size: usize,
+    softmax_scale: f64,
+
+    span: tracing::Span,
+}
+
+impl JinaCodeAttention {
+    pub fn load(vb: VarBuilder, config: &BertConfig) -> Result<Self> {
+        let attention_head_size = config.hidden_size / config.num_attention_heads;
+        let all_head_size = config.num_attention_heads * attention_head_size;
+        let hidden_size = config.hidden_size;
+
+        let query_weight = vb
+            .pp("self.query")
+            .get((all_head_size, hidden_size), "weight")?;
+        let query_bias = vb.pp("self.query").get(all_head_size, "bias")?;
+
+        let key_weight = vb
+            .pp("self.key")
+            .get((all_head_size, hidden_size), "weight")?;
+        let key_bias = vb.pp("self.key").get(all_head_size, "bias")?;
+
+        let value_weight = vb
+            .pp("self.value")
+            .get((all_head_size, hidden_size), "weight")?;
+        let value_bias = vb.pp("self.value").get(all_head_size, "bias")?;
+
+        let qkv_weight = Tensor::cat(&[&query_weight, &key_weight, &value_weight], 0)?;
+        let qkv_bias = Tensor::cat(&[&query_bias, &key_bias, &value_bias], 0)?;
+
+        let qkv_linear = Linear::new(qkv_weight, Some(qkv_bias), None);
+
+        let layer_norm_q = LayerNorm::load(
+            vb.pp("self").pp("layer_norm_q"),
+            config.hidden_size,
+            config.layer_norm_eps as f32,
+        )?;
+        let layer_norm_k = LayerNorm::load(
+            vb.pp("self").pp("layer_norm_k"),
+            config.hidden_size,
+            config.layer_norm_eps as f32,
+        )?;
+
+        let dense_weight = vb
+            .pp("output")
+            .pp("dense")
+            .get((hidden_size, hidden_size), "weight")?;
+        let dense_bias = vb.pp("output").pp("dense").get(hidden_size, "bias")?;
+
+        let dense = Linear::new(dense_weight, Some(dense_bias), None);
+
+        let layer_norm_out = LayerNorm::load(
+            vb.pp("output").pp("LayerNorm"),
+            config.hidden_size,
+            config.layer_norm_eps as f32,
+        )?;
+
+        let softmax_scale = 1. / (attention_head_size as f64).sqrt();
+
+        Ok(Self {
+            qkv_linear,
+            dense,
+            layer_norm_q,
+            layer_norm_k,
+            layer_norm_out,
+            num_attention_heads: config.num_attention_heads,
+            attention_head_size,
+            softmax_scale,
+            span: tracing::span!(tracing::Level::TRACE, "attention"),
+        })
+    }
+
+    fn forward(&self, hidden_states: &Tensor, attention_bias: Option<&Tensor>) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let device = hidden_states.device();
+        let residual = hidden_states.clone();
+
+        let qkv = self.qkv_linear.forward(hidden_states)?;
+
+        let mut new_qkv_shape = qkv.dims().to_vec();
+        new_qkv_shape.pop();
+        new_qkv_shape.push(self.num_attention_heads * 3);
+        new_qkv_shape.push(self.attention_head_size);
+
+        let qkv = qkv.reshape(new_qkv_shape.as_slice())?;
+
+        // Split heads
+        let qkv = qkv.chunk(3, 2)?;
+
+        // Flatten last dims again to go through the layer norm
+        let query_layer = &qkv[0].flatten_from(D::Minus2)?;
+        let key_layer = &qkv[1].flatten_from(D::Minus2)?;
+
+        // Layer norm on q and k
+        let query_layer = self.layer_norm_q.forward(query_layer, None)?;
+        let key_layer = self.layer_norm_k.forward(key_layer, None)?;
+
+        let mut new_qk_shape = query_layer.dims().to_vec();
+        new_qk_shape.pop();
+        new_qk_shape.push(self.num_attention_heads);
+        new_qk_shape.push(self.attention_head_size);
+
+        let query_layer = query_layer
+            .reshape(new_qk_shape.as_slice())?
+            .transpose(1, 2)?
+            .contiguous()?;
+        let key_layer = key_layer
+            .reshape(new_qk_shape.as_slice())?
+            .transpose(1, 2)?
+            .contiguous()?;
+        let value_layer = &qkv[2].transpose(1, 2)?.contiguous()?;
+
+        #[allow(unused_variables)]
+        let context_layer = if let (Device::Cuda(_), Some(cublaslt)) =
+            (device, get_cublas_lt_wrapper())
+        {
+            #[cfg(feature = "cuda")]
+            {
+                // cuBLASLt batch matmul implementation requires inputs to be dims3
+                let (batch_size, _, seq_len, _) = key_layer.shape().dims4()?;
+                let key_layer = key_layer.flatten(0, 1)?;
+                let query_layer = query_layer.flatten(0, 1)?;
+                let value_layer = value_layer.flatten(0, 1)?;
+                let attention_bias = attention_bias.map(|mask| mask.flatten(0, 1)).transpose()?;
+
+                // If attention_bias is set, we fuse the add by giving it as the output matrix
+                // and setting beta to 1.0
+                let beta = match attention_bias.is_some() {
+                    true => Some(1.0),
+                    false => None,
+                };
+
+                // Batch matrix multiplication
+                // Fuse softmax scale and attention_bias add
+                let attention_scores = cublaslt.batch_matmul(
+                    &key_layer,
+                    &query_layer,
+                    attention_bias.as_ref(),
+                    Some(self.softmax_scale as f32),
+                    beta,
+                    None,
+                    None,
+                )?;
+                let attention_probs = candle_nn::ops::softmax_last_dim(&attention_scores)?;
+
+                let context_layer = cublaslt.batch_matmul(
+                    &value_layer.t()?.contiguous()?,
+                    &attention_probs,
+                    // We save one allocation
+                    Some(&query_layer),
+                    None,
+                    None,
+                    None,
+                    None,
+                )?;
+
+                // Reshape to dims4
+                context_layer.reshape((
+                    batch_size,
+                    self.num_attention_heads,
+                    seq_len,
+                    self.attention_head_size,
+                ))
+            }
+            #[cfg(not(feature = "cuda"))]
+            {
+                candle::bail!("`cuda` feature is not enabled")
+            }
+        } else {
+            let attention_scores = query_layer.matmul(&key_layer.t()?)?;
+            let mut attention_scores = (attention_scores * self.softmax_scale)?;
+
+            if let Some(attention_bias) = attention_bias {
+                attention_scores = attention_scores.add(attention_bias)?;
+            }
+
+            let attention_probs = candle_nn::ops::softmax_last_dim(&attention_scores)?;
+            attention_probs.matmul(&value_layer.contiguous()?)
+        }?;
+
+        let context_layer = context_layer.transpose(1, 2)?.flatten_from(D::Minus2)?;
+
+        let hidden_states = self.dense.forward(&context_layer)?;
+        let hidden_states = self
+            .layer_norm_out
+            .forward(&hidden_states, Some(&residual))?;
+
+        Ok(hidden_states)
+    }
+}
+
+struct JinaCodeBertLayer {
+    attention: JinaCodeAttention,
+    up_gated_layer: Linear,
+    down_layer: Linear,
+    layer_norm_1: LayerNorm,
+    layer_norm_2: LayerNorm,
+    act: HiddenAct,
+
+    intermediate_size: usize,
+
+    span: tracing::Span,
+}
+
+impl JinaCodeBertLayer {
+    pub fn load(vb: VarBuilder, config: &BertConfig) -> Result<Self> {
+        let attention = JinaCodeAttention::load(vb.pp("attention"), config)?;
+
+        let up_gated_weight = vb
+            .pp("mlp")
+            .pp("up_gated_layer")
+            .get((config.intermediate_size * 2, config.hidden_size), "weight")?;
+        let up_gated_layer = Linear::new(up_gated_weight, None, None);
+
+        let down_weight = vb
+            .pp("mlp")
+            .pp("down_layer")
+            .get((config.hidden_size, config.intermediate_size), "weight")?;
+        let down_bias = vb
+            .pp("mlp")
+            .pp("down_layer")
+            .get(config.hidden_size, "bias")?;
+        let down_layer = Linear::new(down_weight, Some(down_bias), None);
+
+        let layer_norm_1 = LayerNorm::load(
+            vb.pp("layer_norm_1"),
+            config.hidden_size,
+            config.layer_norm_eps as f32,
+        )?;
+        let layer_norm_2 = LayerNorm::load(
+            vb.pp("layer_norm_2"),
+            config.hidden_size,
+            config.layer_norm_eps as f32,
+        )?;
+
+        Ok(Self {
+            attention,
+            up_gated_layer,
+            down_layer,
+            layer_norm_1,
+            layer_norm_2,
+            act: config.hidden_act.clone(),
+            intermediate_size: config.intermediate_size,
+            span: tracing::span!(tracing::Level::TRACE, "layer"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        attention_bias: Option<&Tensor>,
+    ) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        // Pre-Norm
+        let residual = hidden_states.clone();
+
+        // Self-Attention block
+        let hidden_states = self.attention.forward(hidden_states, attention_bias)?;
+
+        // Pre-MLP LayerNorm
+        let hidden_states = self.layer_norm_1.forward(&hidden_states, Some(&residual))?;
+
+        // MLP block
+        let residual = hidden_states.clone();
+        let hidden_states = self.up_gated_layer.forward(&hidden_states)?;
+        let non_gated = hidden_states.i((.., .., 0..self.intermediate_size))?;
+        let gated = hidden_states.i((.., .., self.intermediate_size..))?;
+        let gated = match self.act {
+            HiddenAct::Gelu => gated.gelu(),
+            HiddenAct::Relu => gated.relu(),
+            HiddenAct::Swiglu => gated.silu(),
+        }?;
+        let hidden_states = (non_gated * gated)?;
+        let hidden_states = self.down_layer.forward(&hidden_states)?;
+
+        // Post-MLP LayerNorm
+        let hidden_states = self.layer_norm_2.forward(&hidden_states, Some(&residual))?;
+
+        Ok(hidden_states)
+    }
+}
+
+struct JinaCodeBertEncoder {
+    layers: Vec<JinaCodeBertLayer>,
+    span: tracing::Span,
+}
+
+impl JinaCodeBertEncoder {
+    pub fn load(vb: VarBuilder, config: &BertConfig) -> Result<Self> {
+        let layers = (0..config.num_hidden_layers)
+            .map(|index| JinaCodeBertLayer::load(vb.pp(format!("layer.{index}")), config))
+            .collect::<Result<Vec<_>>>()?;
+        let span = tracing::span!(tracing::Level::TRACE, "encoder");
+
+        Ok(JinaCodeBertEncoder { layers, span })
+    }
+
+    fn forward(&self, hidden_states: &Tensor, attention_bias: Option<&Tensor>) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let mut hidden_states = hidden_states.clone();
+
+        // Use a loop rather than a fold as it's easier to modify when adding debug/...
+        for layer in self.layers.iter() {
+            hidden_states = layer.forward(&hidden_states, attention_bias)?;
+        }
+
+        Ok(hidden_states)
+    }
+}
+
+pub struct JinaCodeBertModel {
+    embeddings: JinaEmbeddings,
+    encoder: JinaCodeBertEncoder,
+    pool: Pool,
+    alibi: Option<Tensor>,
+
+    num_attention_heads: usize,
+
+    device: Device,
+    dtype: DType,
+
+    span: tracing::Span,
+}
+
+impl JinaCodeBertModel {
+    pub fn load(vb: VarBuilder, config: &BertConfig, model_type: ModelType) -> Result<Self> {
+        let alibi = match config.position_embedding_type {
+            PositionEmbeddingType::Alibi => Some(build_alibi_tensor(
+                config.max_position_embeddings,
+                config.num_attention_heads,
+                vb.device(),
+                vb.dtype(),
+            )?),
+            PositionEmbeddingType::Absolute => None,
+            _ => candle::bail!("not supported"),
+        };
+
+        let pool = match model_type {
+            ModelType::Classifier => {
+                candle::bail!("`classifier` model type is not supported for JinaCode")
+            }
+            ModelType::Embedding(pool) => {
+                if pool == Pool::Splade {
+                    candle::bail!("`splade` is not supported for JinaCode")
+                }
+                if pool == Pool::LastToken {
+                    candle::bail!("`last_token` is not supported for JinaCode");
+                }
+                pool
+            }
+        };
+
+        let (embeddings, encoder) = match (
+            JinaEmbeddings::load(vb.pp("embeddings"), config),
+            JinaCodeBertEncoder::load(vb.pp("encoder"), config),
+        ) {
+            (Ok(embeddings), Ok(encoder)) => (embeddings, encoder),
+            (Err(err), _) | (_, Err(err)) => {
+                if let (Ok(embeddings), Ok(encoder)) = (
+                    JinaEmbeddings::load(vb.pp("bert.embeddings"), config),
+                    JinaCodeBertEncoder::load(vb.pp("bert.encoder"), config),
+                ) {
+                    (embeddings, encoder)
+                } else {
+                    return Err(err);
+                }
+            }
+        };
+
+        Ok(Self {
+            embeddings,
+            encoder,
+            pool,
+            alibi,
+            num_attention_heads: config.num_attention_heads,
+            device: vb.device().clone(),
+            dtype: vb.dtype(),
+            span: tracing::span!(tracing::Level::TRACE, "model"),
+        })
+    }
+
+    pub fn forward(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        let _enter = self.span.enter();
+
+        let batch_size = batch.len();
+        let max_length = batch.max_length as usize;
+
+        let shape = (batch_size, max_length);
+
+        let (input_ids, type_ids, position_ids, input_lengths, attention_bias, attention_mask) =
+            if batch_size > 1 {
+                // Prepare padded batch
+                let elems = batch_size * max_length;
+
+                let mut input_ids = Vec::with_capacity(elems);
+                let mut type_ids = Vec::with_capacity(elems);
+                let mut position_ids = Vec::with_capacity(elems);
+                let mut attention_mask = Vec::with_capacity(elems);
+                let mut attention_bias = Vec::with_capacity(elems);
+                let mut input_lengths = Vec::with_capacity(batch_size);
+                // Bool to know if we need to use the attention mask
+                let mut masking = false;
+
+                for i in 0..batch_size {
+                    let start = batch.cumulative_seq_lengths[i] as usize;
+                    let end = batch.cumulative_seq_lengths[i + 1] as usize;
+                    let seq_length = (end - start) as u32;
+                    input_lengths.push(seq_length as f32);
+
+                    // Copy values
+                    for j in start..end {
+                        input_ids.push(batch.input_ids[j]);
+                        type_ids.push(batch.token_type_ids[j]);
+                        position_ids.push(batch.position_ids[j]);
+                        attention_mask.push(1.0_f32);
+                        attention_bias.push(0.0);
+                    }
+
+                    // Add padding if needed
+                    let padding = batch.max_length - seq_length;
+                    if padding > 0 {
+                        // Set bool to use attention mask
+                        masking = true;
+                        for _ in 0..padding {
+                            input_ids.push(0);
+                            type_ids.push(0);
+                            position_ids.push(0);
+                            attention_mask.push(0.0_f32);
+                            attention_bias.push(f32::NEG_INFINITY);
+                        }
+                    }
+                }
+
+                let (attention_bias, attention_mask) = match masking {
+                    true => {
+                        // We only need the mask if we use mean pooling
+                        // For CLS pooling, the bias is enough
+                        let attention_mask = if self.pool == Pool::Mean {
+                            let attention_mask = Tensor::from_vec(
+                                attention_mask,
+                                (batch_size, max_length, 1),
+                                &self.device,
+                            )?
+                            .to_dtype(self.dtype)?;
+
+                            Some(attention_mask)
+                        } else {
+                            None
+                        };
+
+                        let attention_bias = Tensor::from_vec(
+                            attention_bias,
+                            (batch_size, 1, 1, max_length),
+                            &self.device,
+                        )?
+                        .to_dtype(self.dtype)?;
+
+                        // Broadcast once instead of at every layer
+                        let mut attention_bias = attention_bias.broadcast_as((
+                            batch_size,
+                            self.num_attention_heads,
+                            max_length,
+                            max_length,
+                        ))?;
+
+                        // Add alibi tensor
+                        if let Some(alibi) = &self.alibi {
+                            let alibi = alibi
+                                .i((.., .., 0..max_length, 0..max_length))?
+                                .broadcast_as((
+                                    batch_size,
+                                    self.num_attention_heads,
+                                    max_length,
+                                    max_length,
+                                ))?;
+
+                            attention_bias = attention_bias.add(&alibi)?;
+                        }
+
+                        (Some(attention_bias.contiguous()?), attention_mask)
+                    }
+                    false => {
+                        if let Some(alibi) = &self.alibi {
+                            (
+                                Some(
+                                    alibi
+                                        .i((.., .., 0..max_length, 0..max_length))?
+                                        .broadcast_as((
+                                            batch_size,
+                                            self.num_attention_heads,
+                                            max_length,
+                                            max_length,
+                                        ))?
+                                        .contiguous()?,
+                                ),
+                                None,
+                            )
+                        } else {
+                            (None, None)
+                        }
+                    }
+                };
+
+                (
+                    input_ids,
+                    type_ids,
+                    position_ids,
+                    input_lengths,
+                    attention_bias,
+                    attention_mask,
+                )
+            } else {
+                let attention_bias = if let Some(alibi) = &self.alibi {
+                    Some(
+                        alibi
+                            .i((.., .., 0..max_length, 0..max_length))?
+                            .contiguous()?,
+                    )
+                } else {
+                    None
+                };
+
+                (
+                    batch.input_ids,
+                    batch.token_type_ids,
+                    batch.position_ids,
+                    vec![batch.max_length as f32],
+                    attention_bias,
+                    None,
+                )
+            };
+
+        // Create CPU tensors
+        let input_ids = Tensor::from_vec(input_ids, shape, &self.device)?;
+        let type_ids = Tensor::from_vec(type_ids, shape, &self.device)?;
+        let position_ids = Tensor::from_vec(position_ids, shape, &self.device)?;
+        let input_lengths =
+            Tensor::from_vec(input_lengths, (batch_size, 1), &self.device)?.to_dtype(self.dtype)?;
+
+        let embedding_output = self
+            .embeddings
+            .forward(&input_ids, &type_ids, &position_ids)?;
+
+        let outputs = self
+            .encoder
+            .forward(&embedding_output, attention_bias.as_ref())?;
+
+        let has_pooling_requests = !batch.pooled_indices.is_empty();
+        let has_raw_requests = !batch.raw_indices.is_empty();
+
+        let pooled_embeddings = if has_pooling_requests {
+            let pooled_indices_length = batch.pooled_indices.len();
+            let mut outputs = outputs.clone();
+
+            // Only use pooled_indices if at least one member of the batch ask for raw embeddings
+            let pooled_indices = if has_raw_requests {
+                let pooled_indices =
+                    Tensor::from_vec(batch.pooled_indices, pooled_indices_length, &self.device)?;
+
+                // Select values in the batch
+                outputs = outputs.index_select(&pooled_indices, 0)?;
+                Some(pooled_indices)
+            } else {
+                None
+            };
+
+            let pooled_embeddings = match self.pool {
+                // CLS pooling
+                Pool::Cls => outputs.i((.., 0))?,
+                // Last token pooling is not supported for this model
+                Pool::LastToken => unreachable!(),
+                // Mean pooling
+                Pool::Mean => {
+                    if let Some(ref attention_mask) = attention_mask {
+                        let mut attention_mask = attention_mask.clone();
+
+                        if let Some(pooled_indices) = pooled_indices {
+                            // Select values in the batch
+                            attention_mask = attention_mask.index_select(&pooled_indices, 0)?;
+                        };
+
+                        // Mask padded values
+                        outputs = outputs.broadcast_mul(&attention_mask)?;
+                    }
+
+                    (outputs.sum(1)?.broadcast_div(&input_lengths))?
+                }
+                Pool::Splade => unreachable!(),
+            };
+            Some(pooled_embeddings)
+        } else {
+            None
+        };
+
+        let raw_embeddings = if has_raw_requests {
+            // Reshape outputs
+            let (b, l, h) = outputs.shape().dims3()?;
+            let outputs = outputs.reshape((b * l, h))?;
+
+            // We need to remove the padding tokens only if batch_size > 1 and there are some
+            // member of the batch that require pooling
+            // or if batch_size > 1 and the members of the batch have different lengths
+            if (attention_mask.is_some() || has_pooling_requests) && batch_size > 1 {
+                let mut final_indices: Vec<u32> = Vec::with_capacity(batch_size * max_length);
+
+                for i in batch.raw_indices.into_iter() {
+                    let start = i * batch.max_length;
+                    let i = i as usize;
+                    let length =
+                        batch.cumulative_seq_lengths[i + 1] - batch.cumulative_seq_lengths[i];
+
+                    for j in start..start + length {
+                        // Add indices for the tokens of this specific member of the batch
+                        final_indices.push(j);
+                    }
+                }
+
+                let final_indices_length = final_indices.len();
+                let final_indices =
+                    Tensor::from_vec(final_indices, final_indices_length, &self.device)?;
+
+                // Select the tokens with final indices
+                Some(outputs.index_select(&final_indices, 0)?)
+            } else {
+                Some(outputs)
+            }
+        } else {
+            None
+        };
+
+        Ok((pooled_embeddings, raw_embeddings))
+    }
+}
+
+impl Model for JinaCodeBertModel {
+    fn is_padded(&self) -> bool {
+        true
+    }
+    fn embed(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        self.forward(batch)
+    }
+}
diff --git a/backends/candle/src/models/mistral.rs b/backends/candle/src/models/mistral.rs
new file mode 100644
index 00000000..33c5ab00
--- /dev/null
+++ b/backends/candle/src/models/mistral.rs
@@ -0,0 +1,19 @@
+use crate::layers::HiddenAct;
+use serde::Deserialize;
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct MistralConfig {
+    pub vocab_size: usize,
+    pub hidden_size: usize,
+    pub intermediate_size: usize,
+    pub num_hidden_layers: usize,
+    pub num_attention_heads: usize,
+    pub num_key_value_heads: usize,
+    pub hidden_act: HiddenAct,
+    pub max_position_embeddings: usize,
+    pub initializer_range: f64,
+    pub rms_norm_eps: f32,
+    pub model_type: Option<String>,
+    pub rope_theta: f32,
+    pub sliding_window: Option<usize>,
+}
diff --git a/backends/candle/src/models.rs b/backends/candle/src/models/mod.rs
similarity index 65%
rename from backends/candle/src/models.rs
rename to backends/candle/src/models/mod.rs
index ed89482e..b1e9f937 100644
--- a/backends/candle/src/models.rs
+++ b/backends/candle/src/models/mod.rs
@@ -7,6 +7,8 @@ extern crate accelerate_src;
 mod bert;
 mod distilbert;
 mod jina;
+mod jina_code;
+mod mistral;
 mod nomic;
 
 #[cfg(feature = "cuda")]
@@ -15,17 +17,35 @@ mod flash_bert;
 #[cfg(feature = "cuda")]
 mod flash_jina;
 
+#[cfg(feature = "cuda")]
+mod flash_jina_code;
+
 #[cfg(feature = "cuda")]
 mod flash_nomic;
 
 #[cfg(feature = "cuda")]
 mod flash_distilbert;
 
+#[cfg(feature = "cuda")]
+mod flash_gte;
+#[cfg(feature = "cuda")]
+mod flash_mistral;
+
+#[cfg(feature = "cuda")]
+mod flash_qwen2;
+mod gte;
+mod qwen2;
+
 pub use bert::{BertConfig, BertModel, PositionEmbeddingType};
 use candle::{Result, Tensor};
 pub use distilbert::{DistilBertConfig, DistilBertModel};
+#[allow(unused_imports)]
+pub use gte::{GTEConfig, NTKScaling, RopeScaling};
 pub use jina::JinaBertModel;
+pub use jina_code::JinaCodeBertModel;
+pub use mistral::MistralConfig;
 pub use nomic::{NomicBertModel, NomicConfig};
+pub use qwen2::Qwen2Config;
 use text_embeddings_backend_core::Batch;
 
 #[cfg(feature = "cuda")]
@@ -34,12 +54,24 @@ pub use flash_bert::FlashBertModel;
 #[cfg(feature = "cuda")]
 pub use flash_jina::FlashJinaBertModel;
 
+#[cfg(feature = "cuda")]
+pub use flash_jina_code::FlashJinaCodeBertModel;
+
 #[cfg(feature = "cuda")]
 pub use flash_nomic::FlashNomicBertModel;
 
 #[cfg(feature = "cuda")]
 pub use flash_distilbert::FlashDistilBertModel;
 
+#[cfg(feature = "cuda")]
+pub use flash_mistral::FlashMistralModel;
+
+#[cfg(feature = "cuda")]
+pub use flash_gte::FlashGTEModel;
+
+#[cfg(feature = "cuda")]
+pub use flash_qwen2::FlashQwen2Model;
+
 pub(crate) trait Model {
     fn is_padded(&self) -> bool;
 
diff --git a/backends/candle/src/models/nomic.rs b/backends/candle/src/models/nomic.rs
index 4f9e7551..cdaaea92 100644
--- a/backends/candle/src/models/nomic.rs
+++ b/backends/candle/src/models/nomic.rs
@@ -405,6 +405,9 @@ impl NomicBertModel {
                 if pool == Pool::Splade {
                     candle::bail!("`splade` is not supported for Nomic")
                 }
+                if pool == Pool::LastToken {
+                    candle::bail!("`last_token` is not supported for Nomic");
+                }
                 pool
             }
         };
@@ -610,6 +613,8 @@ impl NomicBertModel {
             let pooled_embeddings = match self.pool {
                 // CLS pooling
                 Pool::Cls => outputs.i((.., 0))?,
+                // Last token pooling is not supported for this model
+                Pool::LastToken => unreachable!(),
                 // Mean pooling
                 Pool::Mean => {
                     if let Some(ref attention_mask) = attention_mask {
diff --git a/backends/candle/src/models/qwen2.rs b/backends/candle/src/models/qwen2.rs
new file mode 100644
index 00000000..8d2d1f86
--- /dev/null
+++ b/backends/candle/src/models/qwen2.rs
@@ -0,0 +1,18 @@
+use crate::layers::HiddenAct;
+use serde::Deserialize;
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct Qwen2Config {
+    pub vocab_size: usize,
+    pub hidden_size: usize,
+    pub intermediate_size: usize,
+    pub num_hidden_layers: usize,
+    pub num_attention_heads: usize,
+    pub num_key_value_heads: usize,
+    pub hidden_act: HiddenAct,
+    pub max_position_embeddings: usize,
+    pub rms_norm_eps: f32,
+    pub rope_theta: f32,
+    pub sliding_window: usize,
+    pub use_sliding_window: bool,
+}
diff --git a/backends/candle/tests/common.rs b/backends/candle/tests/common.rs
index b9d23995..0c069a47 100644
--- a/backends/candle/tests/common.rs
+++ b/backends/candle/tests/common.rs
@@ -1,5 +1,5 @@
 use anyhow::Result;
-use hf_hub::api::sync::ApiBuilder;
+use hf_hub::api::sync::{ApiBuilder, ApiError, ApiRepo};
 use hf_hub::{Repo, RepoType};
 use insta::internals::YamlMatcher;
 use serde::{Deserialize, Serialize};
@@ -51,6 +51,44 @@ impl From<Vec<Vec<f32>>> for SnapshotScores {
     }
 }
 
+#[derive(Serialize, Deserialize, Debug)]
+pub struct SnapEmbedding(Vec<f32>);
+
+impl PartialEq for SnapEmbedding {
+    fn eq(&self, other: &Self) -> bool {
+        assert_eq!(self.0.len(), other.0.len());
+
+        let mut sumxx = 0.0;
+        let mut sumyy = 0.0;
+        let mut sumxy = 0.0;
+
+        for (x, y) in self.0.iter().zip(other.0.iter()) {
+            sumxx += x * x;
+            sumyy += y * y;
+            sumxy += x * y;
+        }
+
+        (sumxy / (sumxx * sumyy).sqrt()) > 0.999
+    }
+}
+
+#[derive(Serialize, Deserialize, Debug, PartialEq)]
+pub struct SnapshotEmbeddings(Vec<SnapEmbedding>);
+
+impl Deref for SnapshotEmbeddings {
+    type Target = Vec<SnapEmbedding>;
+
+    fn deref(&self) -> &Self::Target {
+        &self.0
+    }
+}
+
+impl From<Vec<Vec<f32>>> for SnapshotEmbeddings {
+    fn from(value: Vec<Vec<f32>>) -> Self {
+        Self(value.into_iter().map(|v| SnapEmbedding(v)).collect())
+    }
+}
+
 pub fn sort_embeddings(embeddings: Embeddings) -> (Vec<Vec<f32>>, Vec<Vec<f32>>) {
     let mut pooled_embeddings = Vec::new();
     let mut raw_embeddings = Vec::new();
@@ -69,7 +107,11 @@ pub fn download_artifacts(
     model_id: &'static str,
     revision: Option<&'static str>,
 ) -> Result<PathBuf> {
-    let builder = ApiBuilder::new().with_progress(false);
+    let mut builder = ApiBuilder::new().with_progress(false);
+
+    if let Some(cache_dir) = std::env::var_os("HUGGINGFACE_HUB_CACHE") {
+        builder = builder.with_cache_dir(cache_dir.into());
+    }
 
     let api = builder.build().unwrap();
     let api_repo = if let Some(revision) = revision {
@@ -85,23 +127,66 @@ pub fn download_artifacts(
     api_repo.get("config.json")?;
     api_repo.get("tokenizer.json")?;
 
-    let model_root = match api_repo.get("model.safetensors") {
+    let model_files = match download_safetensors(&api_repo) {
         Ok(p) => p,
         Err(_) => {
+            tracing::warn!("safetensors weights not found. Using `pytorch_model.bin` instead. Model loading will be significantly slower.");
+            tracing::info!("Downloading `pytorch_model.bin`");
             let p = api_repo.get("pytorch_model.bin")?;
-            tracing::warn!("`model.safetensors` not found. Using `pytorch_model.bin` instead. Model loading will be significantly slower.");
-            p
+            vec![p]
         }
-    }
-        .parent().unwrap()
-        .to_path_buf();
+    };
+    let model_root = model_files[0].parent().unwrap().to_path_buf();
     Ok(model_root)
 }
 
+fn download_safetensors(api: &ApiRepo) -> Result<Vec<PathBuf>, ApiError> {
+    // Single file
+    tracing::info!("Downloading `model.safetensors`");
+    match api.get("model.safetensors") {
+        Ok(p) => return Ok(vec![p]),
+        Err(err) => tracing::warn!("Could not download `model.safetensors`: {}", err),
+    };
+
+    // Sharded weights
+    // Download and parse index file
+    tracing::info!("Downloading `model.safetensors.index.json`");
+    let index_file = api.get("model.safetensors.index.json")?;
+    let index_file_string: String =
+        std::fs::read_to_string(index_file).expect("model.safetensors.index.json is corrupted");
+    let json: serde_json::Value = serde_json::from_str(&index_file_string)
+        .expect("model.safetensors.index.json is corrupted");
+
+    let weight_map = match json.get("weight_map") {
+        Some(serde_json::Value::Object(map)) => map,
+        _ => panic!("model.safetensors.index.json is corrupted"),
+    };
+
+    let mut safetensors_filenames = std::collections::HashSet::new();
+    for value in weight_map.values() {
+        if let Some(file) = value.as_str() {
+            safetensors_filenames.insert(file.to_string());
+        }
+    }
+
+    // Download weight files
+    let mut safetensors_files = Vec::new();
+    for n in safetensors_filenames {
+        tracing::info!("Downloading `{}`", n);
+        safetensors_files.push(api.get(&n)?);
+    }
+
+    Ok(safetensors_files)
+}
+
 pub fn relative_matcher() -> YamlMatcher<SnapshotScores> {
     YamlMatcher::new()
 }
 
+pub fn cosine_matcher() -> YamlMatcher<SnapshotEmbeddings> {
+    YamlMatcher::new()
+}
+
 pub fn load_tokenizer(model_root: &Path) -> Result<Tokenizer> {
     // Load tokenizer
     let tokenizer_path = model_root.join("tokenizer.json");
diff --git a/backends/candle/tests/snapshots/test_flash_gte__gte_batch.snap b/backends/candle/tests/snapshots/test_flash_gte__gte_batch.snap
new file mode 100644
index 00000000..d8bbd66e
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_flash_gte__gte_batch.snap
@@ -0,0 +1,2309 @@
+---
+source: backends/candle/tests/test_flash_gte.rs
+assertion_line: 37
+expression: embeddings_batch
+---
+- - 0.4428711
+  - 0.6118164
+  - 0.07043457
+  - -0.5917969
+  - 0.88183594
+  - -0.10839844
+  - 0.0881958
+  - -0.20483398
+  - 1.9345703
+  - 0.6230469
+  - 0.59521484
+  - 0.18737793
+  - 0.030548096
+  - -0.078308105
+  - 0.79589844
+  - -0.23474121
+  - -0.8720703
+  - 1.6923828
+  - 0.12597656
+  - -1.9267578
+  - -0.34472656
+  - -1.9326172
+  - 0.89990234
+  - -0.2006836
+  - 0.024230957
+  - 0.2878418
+  - 0.3642578
+  - 0.17956543
+  - -0.51123047
+  - 0.025817871
+  - -0.8964844
+  - 0.14147949
+  - -0.39257813
+  - -0.8486328
+  - 0.005218506
+  - 0.42358398
+  - 0.23181152
+  - 1.1630859
+  - -1.1455078
+  - 0.3388672
+  - -2.1484375
+  - -1.0146484
+  - -0.2841797
+  - 0.021499634
+  - 0.16235352
+  - 0.39453125
+  - -0.9511719
+  - 0.41064453
+  - -0.15563965
+  - -0.484375
+  - -0.07080078
+  - 0.07312012
+  - 0.56103516
+  - 0.028213501
+  - -0.027938843
+  - 0.43896484
+  - 1.0136719
+  - 0.7788086
+  - -0.5644531
+  - -1.9824219
+  - -0.011169434
+  - 0.50634766
+  - -0.39331055
+  - -0.3334961
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+  - -0.07208252
+  - 0.008544922
+  - 0.7788086
+  - -0.10021973
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+  - 0.35888672
+  - -0.44848633
+  - -0.08679199
+  - -1.0791016
+  - 1.3193359
+  - -1.0273438
+  - 0.34228516
+  - -0.45385742
+  - 0.8027344
+  - 0.50634766
+  - 0.703125
+  - -0.96533203
+  - -0.042938232
+  - -1.0927734
+  - 0.35205078
+  - -0.56689453
+  - -0.5341797
+  - 0.23571777
+  - -2.0234375
+  - -0.26342773
+  - 0.1998291
+  - 0.7739258
+  - 0.17797852
+  - 1.2255859
+  - 0.9814453
+  - -0.2902832
+  - 1.2080078
+  - 0.50097656
+  - 0.2479248
+  - -0.2166748
+  - 0.29052734
+  - -0.5004883
+  - 0.26513672
+  - 0.5024414
+  - -0.042266846
+  - -0.013465881
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+  - 0.64501953
+  - -1.7958984
+  - 0.25512695
+  - -0.20458984
+  - -0.12927246
+  - 0.19665527
+  - 0.8076172
+  - -0.96777344
+  - 0.26391602
+  - -0.5649414
+  - -0.58740234
+  - -0.15466309
+  - 0.7392578
+  - 0.5336914
+  - 0.60253906
+  - 1.1875
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+  - 0.8198242
+  - -0.2401123
+  - 0.40527344
+  - 0.17114258
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+  - 0.056488037
+  - 0.16491699
+  - 0.6899414
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+  - 0.43017578
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+  - 0.040374756
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+  - 0.26831055
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+  - 1.2158203
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+  - 0.6230469
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+  - 0.4819336
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+  - 1.2109375
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+  - 1.2841797
+  - 0.06347656
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+  - 0.3564453
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+  - 0.45336914
+  - -0.28808594
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+  - 0.016067505
+  - 0.63183594
+  - 0.46923828
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+  - 1.7832031
+  - -0.41796875
+  - -0.89453125
+  - 0.07757568
+  - -0.29589844
+  - -1.0673828
+  - 1.3320313
+  - 1.4160156
+  - 0.20703125
+  - -0.1060791
+  - 0.1508789
+  - 0.37231445
+  - 0.05053711
+  - -0.68896484
+  - 0.7397461
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+  - 0.5595703
+  - -0.8198242
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+  - -0.2479248
+  - -0.066345215
+  - 0.4194336
+  - 1.0517578
+  - -1.6650391
+  - 0.90283203
+  - 0.22937012
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+  - -0.79785156
+  - -0.003993988
+  - -0.47314453
+  - 0.19116211
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+  - 0.6333008
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+  - 0.080200195
+  - -1.2587891
+  - 0.22790527
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+  - -0.20471191
+  - 0.75390625
+  - -1.0488281
+  - 0.9902344
+  - 1.2275391
+  - 0.40966797
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+  - 0.54833984
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+  - 0.2208252
+  - 1.1289063
+  - -0.017410278
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+  - 0.7236328
+  - 0.20788574
+  - 0.55566406
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+  - 0.57958984
+  - 1.1494141
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+  - 1.2011719
+  - -1.0029297
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+  - -0.71484375
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+  - 1.1904297
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+  - 0.16992188
+  - 0.8173828
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+  - 1.1376953
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+  - -0.38671875
+  - 0.5625
+  - -0.30493164
+  - 1.8251953
+  - 0.041931152
+  - 0.17285156
+  - -1.0107422
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+  - 1.1005859
+  - 0.7973633
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+  - 0.67041016
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+  - 0.45166016
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+  - -0.97753906
+  - 1.7080078
+  - -0.04626465
+  - 0.4404297
+  - 0.28833008
+  - 0.32006836
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+  - -0.74658203
+  - -1.28125
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+  - 0.88134766
+  - -0.21875
+  - -0.51171875
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+  - 0.6279297
+  - -0.08929443
+  - 1.2490234
+  - -0.3083496
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+  - -0.16223145
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+  - -0.9008789
+  - -0.68896484
+  - -0.55566406
+  - 0.54785156
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+  - 1.0820313
+  - 0.5390625
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+  - -0.0053596497
+  - 0.09838867
+  - -0.08288574
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+  - -0.8120117
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+  - 0.12176514
+  - -1.4072266
+  - 0.6533203
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+  - 0.06011963
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+  - -0.33935547
+  - -0.85058594
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+  - 0.10028076
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+  - 0.98046875
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+  - 0.04675293
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+  - 0.17102051
+  - -0.16772461
+  - -0.10760498
+  - -0.111816406
+  - 0.20251465
+  - -0.65527344
+  - 0.2800293
+  - -1.8037109
+  - 0.15270996
+  - 0.25024414
+  - 0.6713867
+  - 0.67578125
+  - -1.0322266
+  - -0.4140625
+  - -0.59277344
+  - 0.028778076
+  - -1.3515625
+  - 0.12548828
+  - 0.87353516
+  - 0.87060547
+  - -0.18518066
+  - 0.7949219
+  - -0.7011719
+  - -0.28759766
+  - 0.39013672
+  - 0.43139648
+  - -0.053955078
+  - -0.60498047
+  - 0.6381836
+  - 0.20996094
+  - 0.09857178
+  - -0.7402344
+  - 0.24731445
+  - -0.41430664
+  - -0.49365234
+  - -0.7817383
+  - -0.35766602
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diff --git a/backends/candle/tests/snapshots/test_flash_gte__gte_single.snap b/backends/candle/tests/snapshots/test_flash_gte__gte_single.snap
new file mode 100644
index 00000000..96c169b0
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_flash_gte__gte_single.snap
@@ -0,0 +1,773 @@
+---
+source: backends/candle/tests/test_flash_gte.rs
+assertion_line: 48
+expression: embeddings_single
+---
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diff --git a/backends/candle/tests/snapshots/test_flash_jina_code__jina_code_batch.snap b/backends/candle/tests/snapshots/test_flash_jina_code__jina_code_batch.snap
new file mode 100644
index 00000000..0ad889a9
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_flash_jina_code__jina_code_batch.snap
@@ -0,0 +1,2309 @@
+---
+source: backends/candle/tests/test_flash_jina_code.rs
+assertion_line: 37
+expression: embeddings_batch
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diff --git a/backends/candle/tests/snapshots/test_flash_jina_code__jina_code_single.snap b/backends/candle/tests/snapshots/test_flash_jina_code__jina_code_single.snap
new file mode 100644
index 00000000..7d7d6819
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_flash_jina_code__jina_code_single.snap
@@ -0,0 +1,773 @@
+---
+source: backends/candle/tests/test_flash_jina_code.rs
+assertion_line: 48
+expression: embeddings_single
+---
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diff --git a/backends/candle/tests/snapshots/test_flash_mistral__mistral_batch.snap b/backends/candle/tests/snapshots/test_flash_mistral__mistral_batch.snap
new file mode 100644
index 00000000..acea099b
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_flash_mistral__mistral_batch.snap
@@ -0,0 +1,12293 @@
+---
+source: backends/candle/tests/test_flash_mistral.rs
+assertion_line: 37
+expression: embeddings_batch
+---
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diff --git a/backends/candle/tests/snapshots/test_flash_mistral__mistral_single.snap b/backends/candle/tests/snapshots/test_flash_mistral__mistral_single.snap
new file mode 100644
index 00000000..b238aa6a
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_flash_mistral__mistral_single.snap
@@ -0,0 +1,4101 @@
+---
+source: backends/candle/tests/test_flash_mistral.rs
+assertion_line: 48
+expression: embeddings_single
+---
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diff --git a/backends/candle/tests/snapshots/test_flash_qwen2__qwen2_batch.snap b/backends/candle/tests/snapshots/test_flash_qwen2__qwen2_batch.snap
new file mode 100644
index 00000000..7a252f73
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_flash_qwen2__qwen2_batch.snap
@@ -0,0 +1,4613 @@
+---
+source: backends/candle/tests/test_flash_qwen2.rs
+assertion_line: 61
+expression: embeddings_batch
+---
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diff --git a/backends/candle/tests/snapshots/test_flash_qwen2__qwen2_single.snap b/backends/candle/tests/snapshots/test_flash_qwen2__qwen2_single.snap
new file mode 100644
index 00000000..362e1ebf
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_flash_qwen2__qwen2_single.snap
@@ -0,0 +1,1541 @@
+---
+source: backends/candle/tests/test_flash_qwen2.rs
+assertion_line: 72
+expression: embeddings_single
+---
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diff --git a/backends/candle/tests/snapshots/test_jina_code__jina_code_batch.snap b/backends/candle/tests/snapshots/test_jina_code__jina_code_batch.snap
new file mode 100644
index 00000000..c82cb3b7
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_jina_code__jina_code_batch.snap
@@ -0,0 +1,2309 @@
+---
+source: backends/candle/tests/test_jina_code.rs
+assertion_line: 34
+expression: embeddings_batch
+---
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diff --git a/backends/candle/tests/snapshots/test_jina_code__jina_code_single.snap b/backends/candle/tests/snapshots/test_jina_code__jina_code_single.snap
new file mode 100644
index 00000000..dc2119de
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_jina_code__jina_code_single.snap
@@ -0,0 +1,773 @@
+---
+source: backends/candle/tests/test_jina_code.rs
+assertion_line: 45
+expression: embeddings_single
+---
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+  - 1.1900126
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+  - 0.15096185
+  - -0.3360112
+  - 0.5840267
+  - 0.4026988
+  - -0.29323298
+  - -0.123310395
+  - -0.19022875
+  - -0.26955125
+  - -0.34561172
+  - 0.12637521
+  - -0.3267415
+  - -0.24046628
+  - -0.36392075
+  - -0.27500254
+  - -0.12855366
+  - 0.24339654
+  - -0.8961821
+  - -0.30471975
+  - -0.33182493
+  - 0.2114005
+  - 0.008176226
+  - 0.49128672
+  - -0.60276794
+  - 0.85795695
+  - -0.11586124
+  - 0.9756452
+  - -0.6629899
+  - -0.6875797
+  - 0.28862423
+  - -0.18273696
+  - -0.46361175
+  - 0.32340184
+  - 0.27743196
+  - 0.1907873
+  - 1.0107535
+  - -0.36158037
+  - 0.7490063
+  - 0.7224286
+  - 0.8227676
+  - -0.34258455
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+  - 0.66835594
+  - -0.29041466
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+  - 0.22068782
+  - -0.41664478
+  - -0.96687233
+  - -0.30115366
+  - 0.32991317
+  - -0.5367237
+  - -0.4183496
+  - -0.20272818
+  - -0.20603004
+  - 0.28951097
+  - 0.22901037
+  - -0.37840658
+  - 0.039217196
+  - -0.6748186
+  - 0.5217078
+  - -0.10349636
+  - -0.80346566
+  - -0.011289856
+  - 0.34773663
+  - 0.11336774
+  - -0.64846295
+  - -0.060686886
+  - 0.24654308
+  - -0.1032119
+  - 0.70251036
+  - 0.29660708
+  - 0.38687375
+  - -0.13210155
+  - -0.25983995
+  - -0.19132254
+  - 0.22228907
+  - 0.26038757
+  - 0.21548831
+  - 0.14805342
+  - -0.15419401
+  - 0.52764213
+  - 0.056747828
+  - -0.64545715
+  - 0.34790152
+  - 0.09949671
+  - -0.084178016
+  - -0.1851547
+  - 0.18506077
+  - 0.06849785
+  - 0.65021807
+  - -0.21207952
+  - -0.015461175
+  - 0.5452032
+  - -0.01365122
+  - -0.6641938
+  - 0.28765276
+  - -0.25317904
+  - 0.1227632
+  - -1.0584215
+  - 0.20806229
+  - 0.014421382
+  - 0.01081426
+  - -0.02135765
+  - -0.6633148
+  - 0.59829956
+  - 0.32118934
+  - -0.4120391
+  - -0.016948322
+  - -0.07841795
+  - 0.7843749
+  - -0.21809503
+  - -0.38949728
+  - -0.28977337
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+  - 0.5071659
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+  - -0.14100425
+  - -0.31680706
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+  - 0.08322243
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+  - -0.3920549
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+  - -0.27116972
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+  - -0.15916526
+  - 1.0338844
+  - 0.54367495
+  - -0.1075652
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+  - 0.6246316
+  - -0.16583593
+  - -0.009917642
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+  - -0.23796882
+  - 0.28699717
+  - -0.12484648
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+  - -0.4752689
+  - -0.08142411
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+  - 0.121582344
+  - 0.30579346
+  - -0.011138479
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+  - -0.18199337
+  - -0.14097375
+  - -0.14552434
+  - 0.15069063
+  - -0.9438915
+  - -0.074753724
+  - 1.5525185
+  - 0.7813608
+  - -0.30151537
+  - -0.20538484
+  - -0.13666666
+  - 0.2511893
+  - -0.5155156
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+  - -0.08270305
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+  - 0.16015483
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+  - 0.19073258
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+  - 0.55822307
+  - -0.017653605
+  - 0.5665084
+  - -1.0098633
+  - -0.1526397
+  - -1.37959
+  - 0.013235414
+  - -0.96332794
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diff --git a/backends/candle/tests/test_bert.rs b/backends/candle/tests/test_bert.rs
index 45d02577..1bd5017f 100644
--- a/backends/candle/tests/test_bert.rs
+++ b/backends/candle/tests/test_bert.rs
@@ -1,8 +1,8 @@
 mod common;
 
-use crate::common::{sort_embeddings, SnapshotScores};
+use crate::common::{sort_embeddings, SnapshotEmbeddings, SnapshotScores};
 use anyhow::Result;
-use common::{batch, download_artifacts, load_tokenizer, relative_matcher};
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer, relative_matcher};
 use text_embeddings_backend_candle::CandleBackend;
 use text_embeddings_backend_core::{Backend, ModelType, Pool};
 
@@ -28,10 +28,10 @@ fn test_mini() -> Result<()> {
         vec![],
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
-    let embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("mini_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
@@ -41,7 +41,7 @@ fn test_mini() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
 
     insta::assert_yaml_snapshot!("mini_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
@@ -57,8 +57,8 @@ fn test_mini() -> Result<()> {
     );
 
     let (pooled_embeddings, raw_embeddings) = sort_embeddings(backend.embed(input_batch)?);
-    let pooled_embeddings = SnapshotScores::from(pooled_embeddings);
-    let raw_embeddings = SnapshotScores::from(raw_embeddings);
+    let pooled_embeddings = SnapshotEmbeddings::from(pooled_embeddings);
+    let raw_embeddings = SnapshotEmbeddings::from(raw_embeddings);
 
     assert_eq!(embeddings_batch[0], pooled_embeddings[0]);
     assert_eq!(raw_embeddings.len(), 8);
@@ -91,13 +91,13 @@ fn test_mini_pooled_raw() -> Result<()> {
         [1, 4, 5].to_vec(),
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, raw_embeddings) = sort_embeddings(backend.embed(input_batch)?);
-    let pooled_embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let pooled_embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("mini_batch_pooled", pooled_embeddings_batch, &matcher);
 
-    let raw_embeddings_batch = SnapshotScores::from(raw_embeddings);
+    let raw_embeddings_batch = SnapshotEmbeddings::from(raw_embeddings);
     insta::assert_yaml_snapshot!("mini_batch_raw", raw_embeddings_batch, &matcher);
 
     // Check that the first token of each raw embeddings member is the same as the cls pooling ones
@@ -113,7 +113,7 @@ fn test_mini_pooled_raw() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("mini_single_pooled", embeddings_single, &matcher);
 
     assert_eq!(pooled_embeddings_batch[0], embeddings_single[0]);
@@ -126,7 +126,7 @@ fn test_mini_pooled_raw() -> Result<()> {
     );
 
     let (_, raw_embeddings) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(raw_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(raw_embeddings);
     insta::assert_yaml_snapshot!("mini_single_raw", embeddings_single, &matcher);
 
     assert_eq!(raw_embeddings_batch[0], embeddings_single[0]);
diff --git a/backends/candle/tests/test_flash_bert.rs b/backends/candle/tests/test_flash_bert.rs
index 1888a32b..ea150e7f 100644
--- a/backends/candle/tests/test_flash_bert.rs
+++ b/backends/candle/tests/test_flash_bert.rs
@@ -2,9 +2,9 @@
 
 mod common;
 
-use crate::common::{sort_embeddings, SnapshotScores};
+use crate::common::{sort_embeddings, SnapshotEmbeddings, SnapshotScores};
 use anyhow::Result;
-use common::{batch, download_artifacts, load_tokenizer, relative_matcher};
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer, relative_matcher};
 use text_embeddings_backend_candle::CandleBackend;
 use text_embeddings_backend_core::{Backend, ModelType, Pool};
 
@@ -34,10 +34,10 @@ fn test_flash_mini() -> Result<()> {
         vec![],
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
-    let embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("mini_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
@@ -47,7 +47,7 @@ fn test_flash_mini() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
 
     insta::assert_yaml_snapshot!("mini_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
@@ -63,8 +63,8 @@ fn test_flash_mini() -> Result<()> {
     );
 
     let (pooled_embeddings, raw_embeddings) = sort_embeddings(backend.embed(input_batch)?);
-    let pooled_embeddings = SnapshotScores::from(pooled_embeddings);
-    let raw_embeddings = SnapshotScores::from(raw_embeddings);
+    let pooled_embeddings = SnapshotEmbeddings::from(pooled_embeddings);
+    let raw_embeddings = SnapshotEmbeddings::from(raw_embeddings);
 
     assert_eq!(embeddings_batch[0], pooled_embeddings[0]);
     assert_eq!(raw_embeddings.len(), 8);
@@ -101,13 +101,13 @@ fn test_flash_mini_pooled_raw() -> Result<()> {
         [1, 4, 5].to_vec(),
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, raw_embeddings) = sort_embeddings(backend.embed(input_batch)?);
-    let pooled_embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let pooled_embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("mini_batch_pooled", pooled_embeddings_batch, &matcher);
 
-    let raw_embeddings_batch = SnapshotScores::from(raw_embeddings);
+    let raw_embeddings_batch = SnapshotEmbeddings::from(raw_embeddings);
     insta::assert_yaml_snapshot!("mini_batch_raw", raw_embeddings_batch, &matcher);
 
     // Check that the first token of each raw embeddings member is the same as the cls pooling ones
@@ -123,7 +123,7 @@ fn test_flash_mini_pooled_raw() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("mini_single_pooled", embeddings_single, &matcher);
 
     assert_eq!(pooled_embeddings_batch[0], embeddings_single[0]);
@@ -136,7 +136,7 @@ fn test_flash_mini_pooled_raw() -> Result<()> {
     );
 
     let (_, raw_embeddings) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(raw_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(raw_embeddings);
     insta::assert_yaml_snapshot!("mini_single_raw", embeddings_single, &matcher);
 
     assert_eq!(raw_embeddings_batch[0], embeddings_single[0]);
diff --git a/backends/candle/tests/test_flash_gte.rs b/backends/candle/tests/test_flash_gte.rs
new file mode 100644
index 00000000..20b06b2f
--- /dev/null
+++ b/backends/candle/tests/test_flash_gte.rs
@@ -0,0 +1,53 @@
+#![allow(dead_code, unused_imports)]
+mod common;
+
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
+use anyhow::Result;
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
+use text_embeddings_backend_candle::CandleBackend;
+use text_embeddings_backend_core::{Backend, ModelType, Pool};
+
+#[test]
+#[serial_test::serial]
+#[cfg(all(feature = "cuda", feature = "flash-attn"))]
+fn test_flash_gte() -> Result<()> {
+    let model_root = download_artifacts("Alibaba-NLP/gte-base-en-v1.5", None)?;
+    let tokenizer = load_tokenizer(&model_root)?;
+
+    let backend = CandleBackend::new(
+        model_root,
+        "float16".to_string(),
+        ModelType::Embedding(Pool::Cls),
+    )?;
+
+    let input_batch = batch(
+        vec![
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+            tokenizer.encode("Deep Learning is...", true).unwrap(),
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+        ],
+        [0, 1, 2].to_vec(),
+        vec![],
+    );
+
+    let matcher = cosine_matcher();
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
+    insta::assert_yaml_snapshot!("gte_batch", embeddings_batch, &matcher);
+
+    let input_single = batch(
+        vec![tokenizer.encode("What is Deep Learning?", true).unwrap()],
+        [0].to_vec(),
+        vec![],
+    );
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
+
+    insta::assert_yaml_snapshot!("gte_single", embeddings_single, &matcher);
+    assert_eq!(embeddings_batch[0], embeddings_single[0]);
+    assert_eq!(embeddings_batch[2], embeddings_single[0]);
+
+    Ok(())
+}
diff --git a/backends/candle/tests/test_flash_jina.rs b/backends/candle/tests/test_flash_jina.rs
index 4a5f8276..255b82a2 100644
--- a/backends/candle/tests/test_flash_jina.rs
+++ b/backends/candle/tests/test_flash_jina.rs
@@ -1,9 +1,9 @@
 #![allow(dead_code, unused_imports)]
 mod common;
 
-use crate::common::{sort_embeddings, SnapshotScores};
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
 use anyhow::Result;
-use common::{batch, download_artifacts, load_tokenizer, relative_matcher};
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
 use text_embeddings_backend_candle::CandleBackend;
 use text_embeddings_backend_core::{Backend, ModelType, Pool};
 
@@ -30,10 +30,10 @@ fn test_flash_jina_small() -> Result<()> {
         vec![],
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
-    let embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("jina_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
@@ -43,7 +43,7 @@ fn test_flash_jina_small() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
 
     insta::assert_yaml_snapshot!("jina_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
diff --git a/backends/candle/tests/test_flash_jina_code.rs b/backends/candle/tests/test_flash_jina_code.rs
new file mode 100644
index 00000000..d84848dc
--- /dev/null
+++ b/backends/candle/tests/test_flash_jina_code.rs
@@ -0,0 +1,53 @@
+#![allow(dead_code, unused_imports)]
+mod common;
+
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
+use anyhow::Result;
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
+use text_embeddings_backend_candle::CandleBackend;
+use text_embeddings_backend_core::{Backend, ModelType, Pool};
+
+#[test]
+#[serial_test::serial]
+#[cfg(all(feature = "cuda", feature = "flash-attn"))]
+fn test_flash_jina_code_base() -> Result<()> {
+    let model_root = download_artifacts("jinaai/jina-embeddings-v2-base-code", None)?;
+    let tokenizer = load_tokenizer(&model_root)?;
+
+    let backend = CandleBackend::new(
+        model_root,
+        "float16".to_string(),
+        ModelType::Embedding(Pool::Mean),
+    )?;
+
+    let input_batch = batch(
+        vec![
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+            tokenizer.encode("Deep Learning is...", true).unwrap(),
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+        ],
+        [0, 1, 2].to_vec(),
+        vec![],
+    );
+
+    let matcher = cosine_matcher();
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
+    insta::assert_yaml_snapshot!("jina_code_batch", embeddings_batch, &matcher);
+
+    let input_single = batch(
+        vec![tokenizer.encode("What is Deep Learning?", true).unwrap()],
+        [0].to_vec(),
+        vec![],
+    );
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
+
+    insta::assert_yaml_snapshot!("jina_code_single", embeddings_single, &matcher);
+    assert_eq!(embeddings_batch[0], embeddings_single[0]);
+    assert_eq!(embeddings_batch[2], embeddings_single[0]);
+
+    Ok(())
+}
diff --git a/backends/candle/tests/test_flash_mistral.rs b/backends/candle/tests/test_flash_mistral.rs
new file mode 100644
index 00000000..71749c8b
--- /dev/null
+++ b/backends/candle/tests/test_flash_mistral.rs
@@ -0,0 +1,53 @@
+#![allow(dead_code, unused_imports)]
+mod common;
+
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
+use anyhow::Result;
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
+use text_embeddings_backend_candle::CandleBackend;
+use text_embeddings_backend_core::{Backend, ModelType, Pool};
+
+#[test]
+#[serial_test::serial]
+#[cfg(all(feature = "cuda", feature = "flash-attn"))]
+fn test_flash_mistral() -> Result<()> {
+    let model_root = download_artifacts("Salesforce/SFR-Embedding-2_R", None)?;
+    let tokenizer = load_tokenizer(&model_root)?;
+
+    let backend = CandleBackend::new(
+        model_root,
+        "float16".to_string(),
+        ModelType::Embedding(Pool::Mean),
+    )?;
+
+    let input_batch = batch(
+        vec![
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+            tokenizer.encode("Deep Learning is...", true).unwrap(),
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+        ],
+        [0, 1, 2].to_vec(),
+        vec![],
+    );
+
+    let matcher = cosine_matcher();
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
+    insta::assert_yaml_snapshot!("mistral_batch", embeddings_batch, &matcher);
+
+    let input_single = batch(
+        vec![tokenizer.encode("What is Deep Learning?", true).unwrap()],
+        [0].to_vec(),
+        vec![],
+    );
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
+
+    insta::assert_yaml_snapshot!("mistral_single", embeddings_single, &matcher);
+    assert_eq!(embeddings_batch[0], embeddings_single[0]);
+    assert_eq!(embeddings_batch[2], embeddings_single[0]);
+
+    Ok(())
+}
diff --git a/backends/candle/tests/test_flash_nomic.rs b/backends/candle/tests/test_flash_nomic.rs
index 3e9b6e1d..263bbe43 100644
--- a/backends/candle/tests/test_flash_nomic.rs
+++ b/backends/candle/tests/test_flash_nomic.rs
@@ -1,9 +1,9 @@
 #![allow(dead_code, unused_imports)]
 mod common;
 
-use crate::common::{sort_embeddings, SnapshotScores};
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
 use anyhow::Result;
-use common::{batch, download_artifacts, load_tokenizer, relative_matcher};
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
 use text_embeddings_backend_candle::CandleBackend;
 use text_embeddings_backend_core::{Backend, ModelType, Pool};
 
@@ -30,10 +30,10 @@ fn test_flash_nomic_small() -> Result<()> {
         vec![],
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
-    let embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("nomic_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
@@ -43,7 +43,7 @@ fn test_flash_nomic_small() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
 
     insta::assert_yaml_snapshot!("nomic_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
diff --git a/backends/candle/tests/test_flash_qwen2.rs b/backends/candle/tests/test_flash_qwen2.rs
new file mode 100644
index 00000000..38e45553
--- /dev/null
+++ b/backends/candle/tests/test_flash_qwen2.rs
@@ -0,0 +1,77 @@
+#![allow(dead_code, unused_imports)]
+
+mod common;
+
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
+use anyhow::Result;
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
+use text_embeddings_backend_candle::CandleBackend;
+use text_embeddings_backend_core::{Backend, ModelType, Pool};
+use tokenizers::processors::sequence::Sequence;
+use tokenizers::processors::template::TemplateProcessing;
+use tokenizers::{PostProcessorWrapper, Tokenizer};
+
+#[test]
+#[serial_test::serial]
+#[cfg(all(feature = "cuda", feature = "flash-attn"))]
+fn test_flash_qwen2() -> Result<()> {
+    let model_root = download_artifacts("Alibaba-NLP/gte-Qwen2-1.5B-instruct", None)?;
+    let mut tokenizer = load_tokenizer(&model_root)?;
+    // Qwen2 updates the post processor manually instead of into the tokenizer.json...
+    // https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct/blob/main/tokenization_qwen.py#L246
+    let template = TemplateProcessing::builder()
+        .try_single("$A:0 <|endoftext|>:0")
+        .unwrap()
+        .try_pair("$A:0 <|endoftext|>:0 $B:1 <|endoftext|>:1")
+        .unwrap()
+        .special_tokens(vec![("<|endoftext|>", 151643)])
+        .build()
+        .unwrap();
+    match tokenizer.get_post_processor() {
+        None => tokenizer.with_post_processor(template),
+        Some(post_processor) => {
+            let post_processor = Sequence::new(vec![
+                post_processor.clone(),
+                PostProcessorWrapper::Template(template),
+            ]);
+            tokenizer.with_post_processor(post_processor)
+        }
+    };
+
+    let backend = CandleBackend::new(
+        model_root,
+        "float16".to_string(),
+        ModelType::Embedding(Pool::LastToken),
+    )?;
+
+    let input_batch = batch(
+        vec![
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+            tokenizer.encode("Deep Learning is...", true).unwrap(),
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+        ],
+        [0, 1, 2].to_vec(),
+        vec![],
+    );
+
+    let matcher = cosine_matcher();
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
+    insta::assert_yaml_snapshot!("qwen2_batch", embeddings_batch, &matcher);
+
+    let input_single = batch(
+        vec![tokenizer.encode("What is Deep Learning?", true).unwrap()],
+        [0].to_vec(),
+        vec![],
+    );
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
+
+    insta::assert_yaml_snapshot!("qwen2_single", embeddings_single, &matcher);
+    assert_eq!(embeddings_batch[0], embeddings_single[0]);
+    assert_eq!(embeddings_batch[2], embeddings_single[0]);
+
+    Ok(())
+}
diff --git a/backends/candle/tests/test_jina.rs b/backends/candle/tests/test_jina.rs
index 4cd7bba6..4aa30d03 100644
--- a/backends/candle/tests/test_jina.rs
+++ b/backends/candle/tests/test_jina.rs
@@ -1,8 +1,8 @@
 mod common;
 
-use crate::common::{sort_embeddings, SnapshotScores};
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
 use anyhow::Result;
-use common::{batch, download_artifacts, load_tokenizer, relative_matcher};
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
 use text_embeddings_backend_candle::CandleBackend;
 use text_embeddings_backend_core::{Backend, ModelType, Pool};
 
@@ -27,10 +27,10 @@ fn test_jina_small() -> Result<()> {
         vec![],
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
-    let embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("jina_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
@@ -40,7 +40,7 @@ fn test_jina_small() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
 
     insta::assert_yaml_snapshot!("jina_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
diff --git a/backends/candle/tests/test_jina_code.rs b/backends/candle/tests/test_jina_code.rs
new file mode 100644
index 00000000..6c3b3f20
--- /dev/null
+++ b/backends/candle/tests/test_jina_code.rs
@@ -0,0 +1,50 @@
+mod common;
+
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
+use anyhow::Result;
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
+use text_embeddings_backend_candle::CandleBackend;
+use text_embeddings_backend_core::{Backend, ModelType, Pool};
+
+#[test]
+fn test_jina_code_base() -> Result<()> {
+    let model_root = download_artifacts("jinaai/jina-embeddings-v2-base-code", None)?;
+    let tokenizer = load_tokenizer(&model_root)?;
+
+    let backend = CandleBackend::new(
+        model_root,
+        "float32".to_string(),
+        ModelType::Embedding(Pool::Mean),
+    )?;
+
+    let input_batch = batch(
+        vec![
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+            tokenizer.encode("Deep Learning is...", true).unwrap(),
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+        ],
+        [0, 1, 2].to_vec(),
+        vec![],
+    );
+
+    let matcher = cosine_matcher();
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
+    insta::assert_yaml_snapshot!("jina_code_batch", embeddings_batch, &matcher);
+
+    let input_single = batch(
+        vec![tokenizer.encode("What is Deep Learning?", true).unwrap()],
+        [0].to_vec(),
+        vec![],
+    );
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
+
+    insta::assert_yaml_snapshot!("jina_code_single", embeddings_single, &matcher);
+    assert_eq!(embeddings_batch[0], embeddings_single[0]);
+    assert_eq!(embeddings_batch[2], embeddings_single[0]);
+
+    Ok(())
+}
diff --git a/backends/candle/tests/test_nomic.rs b/backends/candle/tests/test_nomic.rs
index 914be7ea..ce0a4559 100644
--- a/backends/candle/tests/test_nomic.rs
+++ b/backends/candle/tests/test_nomic.rs
@@ -1,8 +1,8 @@
 mod common;
 
-use crate::common::{sort_embeddings, SnapshotScores};
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
 use anyhow::Result;
-use common::{batch, download_artifacts, load_tokenizer, relative_matcher};
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
 use text_embeddings_backend_candle::CandleBackend;
 use text_embeddings_backend_core::{Backend, ModelType, Pool};
 
@@ -27,10 +27,10 @@ fn test_nomic_small() -> Result<()> {
         vec![],
     );
 
-    let matcher = relative_matcher();
+    let matcher = cosine_matcher();
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
-    let embeddings_batch = SnapshotScores::from(pooled_embeddings);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
     insta::assert_yaml_snapshot!("nomic_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
@@ -40,7 +40,7 @@ fn test_nomic_small() -> Result<()> {
     );
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
-    let embeddings_single = SnapshotScores::from(pooled_embeddings);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
 
     insta::assert_yaml_snapshot!("nomic_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
diff --git a/backends/core/Cargo.toml b/backends/core/Cargo.toml
index 5e330715..754f9526 100644
--- a/backends/core/Cargo.toml
+++ b/backends/core/Cargo.toml
@@ -6,9 +6,9 @@ authors.workspace = true
 homepage.workspace = true
 
 [dependencies]
-thiserror = "^1.0"
-clap = { version = "^4.1", features = ["derive"], optional = true }
-nohash-hasher = "^0.2"
+thiserror = { workspace = true }
+clap = { workspace = true, optional = true }
+nohash-hasher = { workspace = true }
 
 [features]
 clap = ["dep:clap"]
diff --git a/backends/core/src/lib.rs b/backends/core/src/lib.rs
index 06cef3ed..932c0083 100644
--- a/backends/core/src/lib.rs
+++ b/backends/core/src/lib.rs
@@ -63,6 +63,8 @@ pub enum Pool {
     /// This option is only available if the loaded model is a `ForMaskedLM` Transformer
     /// model.
     Splade,
+    /// Select the last token as embedding
+    LastToken,
 }
 
 impl fmt::Display for Pool {
@@ -71,6 +73,7 @@ impl fmt::Display for Pool {
             Pool::Cls => write!(f, "cls"),
             Pool::Mean => write!(f, "mean"),
             Pool::Splade => write!(f, "splade"),
+            Pool::LastToken => write!(f, "last_token"),
         }
     }
 }
diff --git a/backends/python/server/text_embeddings_server/cli.py b/backends/python/server/text_embeddings_server/cli.py
index 4c627515..9497dc20 100644
--- a/backends/python/server/text_embeddings_server/cli.py
+++ b/backends/python/server/text_embeddings_server/cli.py
@@ -23,6 +23,7 @@ def serve(
     logger_level: str = "INFO",
     json_output: bool = False,
     otlp_endpoint: Optional[str] = None,
+    otlp_service_name: str = "text-embeddings-inference.server",
 ):
     # Remove default handler
     logger.remove()
@@ -42,7 +43,7 @@ def serve(
 
     # Setup OpenTelemetry distributed tracing
     if otlp_endpoint is not None:
-        setup_tracing(otlp_endpoint=otlp_endpoint)
+        setup_tracing(otlp_endpoint=otlp_endpoint, otlp_service_name=otlp_service_name)
 
     # Downgrade enum into str for easier management later on
     dtype = None if dtype is None else dtype.value
diff --git a/backends/python/server/text_embeddings_server/utils/tracing.py b/backends/python/server/text_embeddings_server/utils/tracing.py
index 5a2bb3f7..9299e719 100644
--- a/backends/python/server/text_embeddings_server/utils/tracing.py
+++ b/backends/python/server/text_embeddings_server/utils/tracing.py
@@ -54,10 +54,8 @@ def _start_span(self, handler_call_details, context, set_status_on_exception=Fal
         )
 
 
-def setup_tracing(otlp_endpoint: str):
-    resource = Resource.create(
-        attributes={"service.name": f"text-embeddings-inference.server"}
-    )
+def setup_tracing(otlp_endpoint: str, otlp_service_name: str):
+    resource = Resource.create(attributes={"service.name": otlp_service_name})
     span_exporter = OTLPSpanExporter(endpoint=otlp_endpoint, insecure=True)
     span_processor = BatchSpanProcessor(span_exporter)
 
diff --git a/backends/python/src/lib.rs b/backends/python/src/lib.rs
index f3519ee5..195f1d37 100644
--- a/backends/python/src/lib.rs
+++ b/backends/python/src/lib.rs
@@ -22,6 +22,7 @@ impl PythonBackend {
         model_type: ModelType,
         uds_path: String,
         otlp_endpoint: Option<String>,
+        otlp_service_name: String,
     ) -> Result<Self, BackendError> {
         match model_type {
             ModelType::Classifier => {
@@ -37,8 +38,13 @@ impl PythonBackend {
             }
         };
 
-        let backend_process =
-            management::BackendProcess::new(model_path, dtype, &uds_path, otlp_endpoint)?;
+        let backend_process = management::BackendProcess::new(
+            model_path,
+            dtype,
+            &uds_path,
+            otlp_endpoint,
+            otlp_service_name,
+        )?;
         let tokio_runtime = tokio::runtime::Builder::new_current_thread()
             .enable_all()
             .build()
diff --git a/backends/python/src/management.rs b/backends/python/src/management.rs
index ed0c851e..911c6984 100644
--- a/backends/python/src/management.rs
+++ b/backends/python/src/management.rs
@@ -21,6 +21,7 @@ impl BackendProcess {
         dtype: String,
         uds_path: &str,
         otlp_endpoint: Option<String>,
+        otlp_service_name: String,
     ) -> Result<Self, BackendError> {
         // Get UDS path
         let uds = Path::new(uds_path);
@@ -33,21 +34,24 @@ impl BackendProcess {
         // Process args
         let mut python_server_args = vec![
             model_path,
-            "--dtype".to_string(),
+            "--dtype".to_owned(),
             dtype,
-            "--uds-path".to_string(),
-            uds_path.to_string(),
-            "--logger-level".to_string(),
-            "INFO".to_string(),
-            "--json-output".to_string(),
+            "--uds-path".to_owned(),
+            uds_path.to_owned(),
+            "--logger-level".to_owned(),
+            "INFO".to_owned(),
+            "--json-output".to_owned(),
         ];
 
         // OpenTelemetry
         if let Some(otlp_endpoint) = otlp_endpoint {
-            python_server_args.push("--otlp-endpoint".to_string());
+            python_server_args.push("--otlp-endpoint".to_owned());
             python_server_args.push(otlp_endpoint);
         }
 
+        python_server_args.push("--otlp-service-name".to_owned());
+        python_server_args.push(otlp_service_name);
+
         // Copy current process env
         let envs: Vec<(OsString, OsString)> = env::vars_os().collect();
 
@@ -64,7 +68,7 @@ impl BackendProcess {
             Err(err) => {
                 if err.kind() == io::ErrorKind::NotFound {
                     return Err(BackendError::Start(
-                        "python-text-embeddings-server not found in PATH".to_string(),
+                        "python-text-embeddings-server not found in PATH".to_owned(),
                     ));
                 }
                 return Err(BackendError::Start(err.to_string()));
diff --git a/backends/src/lib.rs b/backends/src/lib.rs
index 4456880c..6fced834 100644
--- a/backends/src/lib.rs
+++ b/backends/src/lib.rs
@@ -22,7 +22,7 @@ use text_embeddings_backend_python::PythonBackend;
 #[derive(Debug, Clone)]
 pub struct Backend {
     /// Channel to communicate with the background thread
-    backend_sender: mpsc::UnboundedSender<BackendCommand>,
+    backend_sender: mpsc::Sender<BackendCommand>,
     /// Health status
     health_receiver: watch::Receiver<bool>,
     _backend_thread: Arc<BackendThread>,
@@ -50,8 +50,9 @@ impl Backend {
         model_type: ModelType,
         uds_path: String,
         otlp_endpoint: Option<String>,
+        otlp_service_name: String,
     ) -> Result<Self, BackendError> {
-        let (backend_sender, backend_receiver) = mpsc::unbounded_channel();
+        let (backend_sender, backend_receiver) = mpsc::channel(8);
 
         let backend = init_backend(
             model_path,
@@ -59,6 +60,7 @@ impl Backend {
             model_type.clone(),
             uds_path,
             otlp_endpoint,
+            otlp_service_name,
         )?;
         let padded_model = backend.is_padded();
         let max_batch_size = backend.max_batch_size();
@@ -86,6 +88,7 @@ impl Backend {
             let (sender, receiver) = oneshot::channel();
             self.backend_sender
                 .send(BackendCommand::Health(Span::current(), sender))
+                .await
                 .expect("No backend receiver. This is a bug.");
             receiver.await.expect(
                 "Backend blocking task dropped the sender without sending a response. This is a bug.",
@@ -214,7 +217,7 @@ impl Backend {
     pub async fn embed(&self, batch: Batch) -> Result<(Embeddings, Duration), BackendError> {
         let (sender, receiver) = oneshot::channel();
         self.backend_sender
-            .send(BackendCommand::Embed(batch, Span::current(), sender))
+            .try_send(BackendCommand::Embed(batch, Span::current(), sender))
             .expect("No backend receiver. This is a bug.");
         receiver.await.expect(
             "Backend blocking task dropped the sender without send a response. This is a bug.",
@@ -226,7 +229,7 @@ impl Backend {
         let (sender, receiver) = oneshot::channel();
 
         self.backend_sender
-            .send(BackendCommand::Predict(batch, Span::current(), sender))
+            .try_send(BackendCommand::Predict(batch, Span::current(), sender))
             .expect("No backend receiver. This is a bug.");
         receiver.await.expect(
             "Backend blocking task dropped the sender without send a response. This is a bug.",
@@ -241,6 +244,7 @@ fn init_backend(
     model_type: ModelType,
     uds_path: String,
     otlp_endpoint: Option<String>,
+    otlp_service_name: String,
 ) -> Result<Box<dyn CoreBackend + Send>, BackendError> {
     if cfg!(feature = "candle") {
         #[cfg(feature = "candle")]
@@ -260,6 +264,7 @@ fn init_backend(
                         model_type,
                         uds_path,
                         otlp_endpoint,
+                        otlp_service_name,
                     )
                 })
                 .join()
@@ -276,7 +281,7 @@ struct BackendThread(Option<JoinHandle<()>>);
 impl BackendThread {
     fn new(
         backend: Box<dyn CoreBackend + Send>,
-        mut backend_receiver: mpsc::UnboundedReceiver<BackendCommand>,
+        mut backend_receiver: mpsc::Receiver<BackendCommand>,
         health_sender: watch::Sender<bool>,
     ) -> Self {
         let handle = std::thread::spawn(move || {
diff --git a/core/Cargo.toml b/core/Cargo.toml
index de269bf4..d69871dc 100644
--- a/core/Cargo.toml
+++ b/core/Cargo.toml
@@ -6,10 +6,12 @@ authors.workspace = true
 homepage.workspace = true
 
 [dependencies]
-hf-hub = { version = "^0.3.0", features = ["tokio"], default-features = false }
-metrics = "^0.21"
+async-channel = "^2.3"
+hf-hub = { workspace = true }
+metrics = { workspace = true }
+serde_json = { workspace = true }
 text-embeddings-backend = { path = "../backends" }
-thiserror = "^1.0"
-tokenizers = { version = "^0.15.2", default-features = false, features = ["onig", "esaxx_fast"] }
-tracing = "^0.1"
-tokio = { version = "^1.25", features = ["rt", "rt-multi-thread", "parking_lot", "sync"] }
+thiserror = { workspace = true }
+tokenizers = { workspace = true }
+tracing = { workspace = true }
+tokio = { workspace = true }
diff --git a/core/src/download.rs b/core/src/download.rs
index 9e21961b..08220752 100644
--- a/core/src/download.rs
+++ b/core/src/download.rs
@@ -1,7 +1,4 @@
-use hf_hub::api::{
-    tokio::{ApiError, ApiRepo},
-    RepoInfo,
-};
+use hf_hub::api::tokio::{ApiError, ApiRepo};
 use std::path::PathBuf;
 use tracing::instrument;
 
@@ -16,65 +13,79 @@ pub const ST_CONFIG_NAMES: [&str; 7] = [
     "sentence_xlnet_config.json",
 ];
 
-/// Parses a [`RepoInfo`] object for model weight files to download. Returns a non-empty vector of
-/// model files that contains a model type str, if there are any.
-#[instrument(skip_all)]
-fn _weight_files_to_download<'a>(
-    api_info: &'a RepoInfo,
-    weight_file_type_str: &'a str,
-) -> Option<Vec<&'a str>> {
-    let ignored_file_contains = ["arguments", "args", "training", "medusa_lm_head"];
-    let files: Vec<&str> = api_info
-        .siblings
-        .iter()
-        .map(|s| s.rfilename.as_str())
-        .filter(|f| {
-            f.contains(weight_file_type_str)
-                && f.split("/").count() == 1
-                && ignored_file_contains.iter().all(|s| !f.contains(s))
-        })
-        .collect();
-    if files.is_empty() {
-        return None;
-    }
-    Some(files)
-}
-
 #[instrument(skip_all)]
 pub async fn download_artifacts(api: &ApiRepo) -> Result<PathBuf, ApiError> {
     let start = std::time::Instant::now();
 
     tracing::info!("Starting download");
 
-    let model_root = api
-        .get("config.json")
-        .await?
-        .parent()
-        .unwrap()
-        .to_path_buf();
+    tracing::info!("Downloading `config.json`");
+    api.get("config.json").await?;
+
+    tracing::info!("Downloading `tokenizer.json`");
     api.get("tokenizer.json").await?;
 
-    let api_info = api.info().await?;
-    let model_files = _weight_files_to_download(&api_info, "safetensors")
-        .or_else(|| {
-            tracing::warn!("`model.safetensors` not found. Using `pytorch_model.bin` instead. Model loading will be significantly slower.");
-            _weight_files_to_download(&api_info, "pytorch_model")
-        }).expect("No model files found as `safetensors` or `pytorch_model`");
+    let model_files = match download_safetensors(api).await {
+        Ok(p) => p,
+        Err(_) => {
+            tracing::warn!("safetensors weights not found. Using `pytorch_model.bin` instead. Model loading will be significantly slower.");
+            tracing::info!("Downloading `pytorch_model.bin`");
+            let p = api.get("pytorch_model.bin").await?;
+            vec![p]
+        }
+    };
+    let model_root = model_files[0].parent().unwrap().to_path_buf();
 
-    // Download the model files
-    for file_name in model_files {
-        api.get(file_name).await?;
-    }
     tracing::info!("Model artifacts downloaded in {:?}", start.elapsed());
     Ok(model_root)
 }
 
 #[instrument(skip_all)]
 pub async fn download_pool_config(api: &ApiRepo) -> Result<PathBuf, ApiError> {
+    tracing::info!("Downloading `1_Pooling/config.json`");
     let pool_config_path = api.get("1_Pooling/config.json").await?;
     Ok(pool_config_path)
 }
 
+async fn download_safetensors(api: &ApiRepo) -> Result<Vec<PathBuf>, ApiError> {
+    // Single file
+    tracing::info!("Downloading `model.safetensors`");
+    match api.get("model.safetensors").await {
+        Ok(p) => return Ok(vec![p]),
+        Err(err) => tracing::warn!("Could not download `model.safetensors`: {}", err),
+    };
+
+    // Sharded weights
+    // Download and parse index file
+    tracing::info!("Downloading `model.safetensors.index.json`");
+    let index_file = api.get("model.safetensors.index.json").await?;
+    let index_file_string: String =
+        std::fs::read_to_string(index_file).expect("model.safetensors.index.json is corrupted");
+    let json: serde_json::Value = serde_json::from_str(&index_file_string)
+        .expect("model.safetensors.index.json is corrupted");
+
+    let weight_map = match json.get("weight_map") {
+        Some(serde_json::Value::Object(map)) => map,
+        _ => panic!("model.safetensors.index.json is corrupted"),
+    };
+
+    let mut safetensors_filenames = std::collections::HashSet::new();
+    for value in weight_map.values() {
+        if let Some(file) = value.as_str() {
+            safetensors_filenames.insert(file.to_string());
+        }
+    }
+
+    // Download weight files
+    let mut safetensors_files = Vec::new();
+    for n in safetensors_filenames {
+        tracing::info!("Downloading `{}`", n);
+        safetensors_files.push(api.get(&n).await?);
+    }
+
+    Ok(safetensors_files)
+}
+
 #[instrument(skip_all)]
 pub async fn download_st_config(api: &ApiRepo) -> Result<PathBuf, ApiError> {
     // Try default path
@@ -91,3 +102,10 @@ pub async fn download_st_config(api: &ApiRepo) -> Result<PathBuf, ApiError> {
 
     Err(err)
 }
+
+#[instrument(skip_all)]
+pub async fn download_new_st_config(api: &ApiRepo) -> Result<PathBuf, ApiError> {
+    tracing::info!("Downloading `config_sentence_transformers.json`");
+    let pool_config_path = api.get("config_sentence_transformers.json").await?;
+    Ok(pool_config_path)
+}
diff --git a/core/src/infer.rs b/core/src/infer.rs
index 54f755d9..23b343bf 100644
--- a/core/src/infer.rs
+++ b/core/src/infer.rs
@@ -4,6 +4,7 @@ use crate::TextEmbeddingsError;
 use std::sync::Arc;
 use std::time::{Duration, Instant};
 use text_embeddings_backend::{Backend, BackendError, Embedding, ModelType};
+use tokenizers::TruncationDirection;
 use tokio::sync::{mpsc, oneshot, watch, Notify, OwnedSemaphorePermit, Semaphore};
 use tracing::instrument;
 
@@ -29,14 +30,10 @@ impl Infer {
     ) -> Self {
         let notify_batching_task = Arc::new(Notify::new());
 
-        let (embed_sender, embed_receiver) = mpsc::unbounded_channel();
+        // Bound channel to 1 to be able to prefetch one batch
+        let (embed_sender, embed_receiver) = mpsc::channel(1);
 
-        // Create two batching tasks to prefetch batches
-        tokio::spawn(batching_task(
-            queue.clone(),
-            notify_batching_task.clone(),
-            embed_sender.clone(),
-        ));
+        // Batching task
         tokio::spawn(batching_task(
             queue.clone(),
             notify_batching_task.clone(),
@@ -58,23 +55,25 @@ impl Infer {
         }
     }
 
-    #[instrument(skip(self))]
+    #[instrument(skip(self, inputs))]
     pub async fn tokenize<I: Into<EncodingInput> + std::fmt::Debug>(
         &self,
         inputs: I,
         add_special_tokens: bool,
-    ) -> Result<RawEncoding, TextEmbeddingsError> {
+        prompt_name: Option<String>,
+    ) -> Result<(Option<String>, RawEncoding), TextEmbeddingsError> {
         self.tokenization
-            .tokenize(inputs.into(), add_special_tokens)
+            .tokenize(inputs.into(), add_special_tokens, prompt_name)
             .await
             .map_err(|err| {
-                metrics::increment_counter!("te_request_failure", "err" => "tokenization");
+                let counter = metrics::counter!("te_request_failure", "err" => "tokenization");
+                counter.increment(1);
                 tracing::error!("{err}");
                 err
             })
     }
 
-    #[instrument(skip(self))]
+    #[instrument(skip(self, ids))]
     pub async fn decode(
         &self,
         ids: Vec<u32>,
@@ -84,7 +83,8 @@ impl Infer {
             .decode(ids, skip_special_tokens)
             .await
             .map_err(|err| {
-                metrics::increment_counter!("te_request_failure", "err" => "tokenization");
+                let counter = metrics::counter!("te_request_failure", "err" => "tokenization");
+                counter.increment(1);
                 tracing::error!("{err}");
                 err
             })
@@ -97,11 +97,13 @@ impl Infer {
             .limit_concurrent_requests
             .try_acquire_owned()
             .map_err(|err| {
-                metrics::increment_counter!("te_request_failure", "err" => "overloaded");
+                let counter = metrics::counter!("te_request_failure", "err" => "overloaded");
+                counter.increment(1);
                 tracing::error!("{err}");
                 TextEmbeddingsError::from(err)
             })
     }
+
     #[instrument(skip(self))]
     pub async fn acquire_permit(&self) -> OwnedSemaphorePermit {
         // Limit concurrent requests by acquiring a permit from the semaphore
@@ -112,17 +114,20 @@ impl Infer {
             .expect("Semaphore has been closed. This is a bug.")
     }
 
-    #[instrument(skip(self, permit))]
+    #[instrument(skip(self, inputs, permit))]
     pub async fn embed_all<I: Into<EncodingInput> + std::fmt::Debug>(
         &self,
         inputs: I,
         truncate: bool,
+        truncation_direction: TruncationDirection,
+        prompt_name: Option<String>,
         permit: OwnedSemaphorePermit,
     ) -> Result<AllEmbeddingsInferResponse, TextEmbeddingsError> {
         let start_time = Instant::now();
 
         if self.is_splade() {
-            metrics::increment_counter!("te_request_failure", "err" => "model_type");
+            let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+            counter.increment(1);
             let message = "`embed_all` is not available for SPLADE models".to_string();
             tracing::error!("{message}");
             return Err(TextEmbeddingsError::Backend(BackendError::Inference(
@@ -131,7 +136,15 @@ impl Infer {
         }
 
         let results = self
-            .embed(inputs, truncate, false, &start_time, permit)
+            .embed(
+                inputs,
+                truncate,
+                truncation_direction,
+                prompt_name,
+                false,
+                &start_time,
+                permit,
+            )
             .await?;
 
         let InferResult::AllEmbedding(response) = results else {
@@ -142,35 +155,34 @@ impl Infer {
         let total_time = start_time.elapsed();
 
         // Metrics
-        metrics::increment_counter!("te_embed_success");
-        metrics::histogram!("te_embed_duration", total_time.as_secs_f64());
-        metrics::histogram!(
-            "te_embed_tokenization_duration",
-            response.metadata.tokenization.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_embed_queue_duration",
-            response.metadata.queue.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_embed_inference_duration",
-            response.metadata.inference.as_secs_f64()
-        );
+        let counter = metrics::counter!("te_embed_success");
+        counter.increment(1);
+        let histogram = metrics::histogram!("te_embed_duration");
+        histogram.record(total_time.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_tokenization_duration");
+        histogram.record(response.metadata.tokenization.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_queue_duration");
+        histogram.record(response.metadata.queue.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_inference_duration");
+        histogram.record(response.metadata.inference.as_secs_f64());
 
         Ok(response)
     }
 
-    #[instrument(skip(self, permit))]
+    #[instrument(skip(self, inputs, permit))]
     pub async fn embed_sparse<I: Into<EncodingInput> + std::fmt::Debug>(
         &self,
         inputs: I,
         truncate: bool,
+        truncation_direction: TruncationDirection,
+        prompt_name: Option<String>,
         permit: OwnedSemaphorePermit,
     ) -> Result<PooledEmbeddingsInferResponse, TextEmbeddingsError> {
         let start_time = Instant::now();
 
         if !self.is_splade() {
-            metrics::increment_counter!("te_request_failure", "err" => "model_type");
+            let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+            counter.increment(1);
             let message = "Model is not an embedding model with SPLADE pooling".to_string();
             tracing::error!("{message}");
             return Err(TextEmbeddingsError::Backend(BackendError::Inference(
@@ -179,7 +191,15 @@ impl Infer {
         }
 
         let results = self
-            .embed(inputs, truncate, true, &start_time, permit)
+            .embed(
+                inputs,
+                truncate,
+                truncation_direction,
+                prompt_name,
+                true,
+                &start_time,
+                permit,
+            )
             .await?;
 
         let InferResult::PooledEmbedding(response) = results else {
@@ -190,36 +210,35 @@ impl Infer {
         let total_time = start_time.elapsed();
 
         // Metrics
-        metrics::increment_counter!("te_embed_success");
-        metrics::histogram!("te_embed_duration", total_time.as_secs_f64());
-        metrics::histogram!(
-            "te_embed_tokenization_duration",
-            response.metadata.tokenization.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_embed_queue_duration",
-            response.metadata.queue.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_embed_inference_duration",
-            response.metadata.inference.as_secs_f64()
-        );
+        let counter = metrics::counter!("te_embed_success");
+        counter.increment(1);
+        let histogram = metrics::histogram!("te_embed_duration");
+        histogram.record(total_time.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_tokenization_duration");
+        histogram.record(response.metadata.tokenization.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_queue_duration");
+        histogram.record(response.metadata.queue.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_inference_duration");
+        histogram.record(response.metadata.inference.as_secs_f64());
 
         Ok(response)
     }
 
-    #[instrument(skip(self, permit))]
+    #[instrument(skip(self, inputs, permit))]
     pub async fn embed_pooled<I: Into<EncodingInput> + std::fmt::Debug>(
         &self,
         inputs: I,
         truncate: bool,
+        truncation_direction: TruncationDirection,
+        prompt_name: Option<String>,
         normalize: bool,
         permit: OwnedSemaphorePermit,
     ) -> Result<PooledEmbeddingsInferResponse, TextEmbeddingsError> {
         let start_time = Instant::now();
 
         if self.is_splade() && normalize {
-            metrics::increment_counter!("te_request_failure", "err" => "model_type");
+            let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+            counter.increment(1);
             let message = "`normalize` is not available for SPLADE models".to_string();
             tracing::error!("{message}");
             return Err(TextEmbeddingsError::Backend(BackendError::Inference(
@@ -228,7 +247,15 @@ impl Infer {
         }
 
         let results = self
-            .embed(inputs, truncate, true, &start_time, permit)
+            .embed(
+                inputs,
+                truncate,
+                truncation_direction,
+                prompt_name,
+                true,
+                &start_time,
+                permit,
+            )
             .await?;
 
         let InferResult::PooledEmbedding(mut response) = results else {
@@ -256,34 +283,34 @@ impl Infer {
         let total_time = start_time.elapsed();
 
         // Metrics
-        metrics::increment_counter!("te_embed_success");
-        metrics::histogram!("te_embed_duration", total_time.as_secs_f64());
-        metrics::histogram!(
-            "te_embed_tokenization_duration",
-            response.metadata.tokenization.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_embed_queue_duration",
-            response.metadata.queue.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_embed_inference_duration",
-            response.metadata.inference.as_secs_f64()
-        );
+        let counter = metrics::counter!("te_embed_success");
+        counter.increment(1);
+        let histogram = metrics::histogram!("te_embed_duration");
+        histogram.record(total_time.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_tokenization_duration");
+        histogram.record(response.metadata.tokenization.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_queue_duration");
+        histogram.record(response.metadata.queue.as_secs_f64());
+        let histogram = metrics::histogram!("te_embed_inference_duration");
+        histogram.record(response.metadata.inference.as_secs_f64());
 
         Ok(response)
     }
 
+    #[allow(clippy::too_many_arguments)]
     async fn embed<I: Into<EncodingInput> + std::fmt::Debug>(
         &self,
         inputs: I,
         truncate: bool,
+        truncation_direction: TruncationDirection,
+        prompt_name: Option<String>,
         pooling: bool,
         start_time: &Instant,
         _permit: OwnedSemaphorePermit,
     ) -> Result<InferResult, TextEmbeddingsError> {
         if self.is_classifier() {
-            metrics::increment_counter!("te_request_failure", "err" => "model_type");
+            let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+            counter.increment(1);
             let message = "Model is not an embedding model".to_string();
             tracing::error!("{message}");
             return Err(TextEmbeddingsError::Backend(BackendError::Inference(
@@ -291,15 +318,17 @@ impl Infer {
             )));
         }
 
-        metrics::increment_counter!("te_embed_count");
+        let counter = metrics::counter!("te_embed_count");
+        counter.increment(1);
 
         // Tokenization
         let encoding = self
             .tokenization
-            .encode(inputs.into(), truncate)
+            .encode(inputs.into(), truncate, truncation_direction, prompt_name)
             .await
             .map_err(|err| {
-                metrics::increment_counter!("te_request_failure", "err" => "tokenization");
+                let counter = metrics::counter!("te_request_failure", "err" => "tokenization");
+                counter.increment(1);
                 tracing::error!("{err}");
                 err
             })?;
@@ -327,7 +356,8 @@ impl Infer {
                 "Infer batching task dropped the sender without sending a response. This is a bug.",
             )
             .map_err(|err| {
-                metrics::increment_counter!("te_request_failure", "err" => "inference");
+                let counter = metrics::counter!("te_request_failure", "err" => "inference");
+                counter.increment(1);
                 tracing::error!("{err}");
                 err
             })?;
@@ -335,16 +365,18 @@ impl Infer {
         Ok(response)
     }
 
-    #[instrument(skip(self, _permit))]
+    #[instrument(skip(self, inputs, _permit))]
     pub async fn predict<I: Into<EncodingInput> + std::fmt::Debug>(
         &self,
         inputs: I,
         truncate: bool,
+        truncation_direction: TruncationDirection,
         raw_scores: bool,
         _permit: OwnedSemaphorePermit,
     ) -> Result<ClassificationInferResponse, TextEmbeddingsError> {
         if !self.is_classifier() {
-            metrics::increment_counter!("te_request_failure", "err" => "model_type");
+            let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+            counter.increment(1);
             let message = "Model is not a classifier model".to_string();
             return Err(TextEmbeddingsError::Backend(BackendError::Inference(
                 message,
@@ -352,15 +384,17 @@ impl Infer {
         }
 
         let start_time = Instant::now();
-        metrics::increment_counter!("te_predict_count");
+        let counter = metrics::counter!("te_predict_count");
+        counter.increment(1);
 
         // Tokenization
         let encoding = self
             .tokenization
-            .encode(inputs.into(), truncate)
+            .encode(inputs.into(), truncate, truncation_direction, None)
             .await
             .map_err(|err| {
-                metrics::increment_counter!("te_request_failure", "err" => "tokenization");
+                let counter = metrics::counter!("te_request_failure", "err" => "tokenization");
+                counter.increment(1);
                 tracing::error!("{err}");
                 err
             })?;
@@ -388,7 +422,8 @@ impl Infer {
                 "Infer batching task dropped the sender without sending a response. This is a bug.",
             )
             .map_err(|err| {
-                metrics::increment_counter!("te_request_failure", "err" => "inference");
+                let counter = metrics::counter!("te_request_failure", "err" => "inference");
+                counter.increment(1);
                 tracing::error!("{err}");
                 err
             })?;
@@ -425,20 +460,16 @@ impl Infer {
         let total_time = start_time.elapsed();
 
         // Metrics
-        metrics::increment_counter!("te_predict_success");
-        metrics::histogram!("te_predict_duration", total_time.as_secs_f64());
-        metrics::histogram!(
-            "te_predict_tokenization_duration",
-            response.metadata.tokenization.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_predict_queue_duration",
-            response.metadata.queue.as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_predict_inference_duration",
-            response.metadata.inference.as_secs_f64()
-        );
+        let counter = metrics::counter!("te_predict_success");
+        counter.increment(1);
+        let histogram = metrics::histogram!("te_predict_duration");
+        histogram.record(total_time.as_secs_f64());
+        let histogram = metrics::histogram!("te_predict_tokenization_duration");
+        histogram.record(response.metadata.tokenization.as_secs_f64());
+        let histogram = metrics::histogram!("te_predict_queue_duration");
+        histogram.record(response.metadata.queue.as_secs_f64());
+        let histogram = metrics::histogram!("te_predict_inference_duration");
+        histogram.record(response.metadata.inference.as_secs_f64());
 
         Ok(response)
     }
@@ -468,30 +499,30 @@ impl Infer {
 }
 
 #[instrument(skip_all)]
-async fn batching_task(
-    queue: Queue,
-    notify: Arc<Notify>,
-    embed_sender: mpsc::UnboundedSender<(NextBatch, oneshot::Sender<()>)>,
-) {
+async fn batching_task(queue: Queue, notify: Arc<Notify>, embed_sender: mpsc::Sender<NextBatch>) {
     loop {
         notify.notified().await;
 
-        while let Some(next_batch) = queue.next_batch().await {
-            let (callback_sender, callback_receiver) = oneshot::channel();
-            embed_sender
-                .send((next_batch, callback_sender))
+        {
+            let mut permit = embed_sender
+                .reserve()
+                .await
                 .expect("embed receiver was dropped. This is a bug.");
-            let _ = callback_receiver.await;
+
+            while let Some(next_batch) = queue.next_batch().await {
+                permit.send(next_batch);
+                permit = embed_sender
+                    .reserve()
+                    .await
+                    .expect("embed receiver was dropped. This is a bug.");
+            }
         }
     }
 }
 
 #[instrument(skip_all)]
-async fn backend_task(
-    backend: Backend,
-    mut embed_receiver: mpsc::UnboundedReceiver<(NextBatch, oneshot::Sender<()>)>,
-) {
-    while let Some((batch, _callback)) = embed_receiver.recv().await {
+async fn backend_task(backend: Backend, mut embed_receiver: mpsc::Receiver<NextBatch>) {
+    while let Some(batch) = embed_receiver.recv().await {
         match &backend.model_type {
             ModelType::Classifier => {
                 let results = backend.predict(batch.1).await;
diff --git a/core/src/queue.rs b/core/src/queue.rs
index dfefda21..3fd8b771 100644
--- a/core/src/queue.rs
+++ b/core/src/queue.rs
@@ -35,7 +35,7 @@ pub struct Metadata {
 #[derive(Debug, Clone)]
 pub struct Queue {
     /// Channel to communicate with the background queue task
-    queue_sender: mpsc::UnboundedSender<QueueCommand>,
+    queue_sender: mpsc::Sender<QueueCommand>,
 }
 
 impl Queue {
@@ -46,7 +46,7 @@ impl Queue {
         max_concurrent_requests: usize,
     ) -> Self {
         // Create channels
-        let (queue_sender, queue_receiver) = mpsc::unbounded_channel();
+        let (queue_sender, queue_receiver) = mpsc::channel(max_concurrent_requests);
 
         // Launch background queue task
         std::thread::spawn(move || {
@@ -68,8 +68,8 @@ impl Queue {
         // Send append command to the background task managing the state
         // Unwrap is safe here
         self.queue_sender
-            .send(QueueCommand::Append(Box::new(entry), Span::current()))
-            .expect("Queue background task dropped the receiver. This is a bug.");
+            .try_send(QueueCommand::Append(Box::new(entry), Span::current()))
+            .expect("Queue background task dropped the receiver or the receiver is too behind. This is a bug.");
     }
 
     /// Get the next batch from the queue
@@ -80,11 +80,11 @@ impl Queue {
         // Send next batch command to the background task managing the state
         // Unwrap is safe here
         self.queue_sender
-            .send(QueueCommand::NextBatch {
+            .try_send(QueueCommand::NextBatch {
                 response_sender,
                 span: Span::current(),
             })
-            .expect("Queue background task dropped the receiver. This is a bug.");
+            .expect("Queue background task dropped the receiver or the receiver is too behind. This is a bug.");
         // Await on response channel
         // Unwrap is safe here
         response_receiver.await.expect(
@@ -99,7 +99,7 @@ fn queue_blocking_task(
     max_batch_tokens: usize,
     max_batch_requests: Option<usize>,
     max_concurrent_requests: usize,
-    mut queue_receiver: mpsc::UnboundedReceiver<QueueCommand>,
+    mut queue_receiver: mpsc::Receiver<QueueCommand>,
 ) {
     let capacity = max_batch_requests.unwrap_or(max_concurrent_requests);
 
@@ -110,7 +110,8 @@ fn queue_blocking_task(
             QueueCommand::Append(entry, span) => {
                 let _span = span.entered();
                 entries.push_back(*entry);
-                metrics::increment_gauge!("te_queue_size", 1.0);
+                let gauge = metrics::gauge!("te_queue_size");
+                gauge.increment(1.0);
             }
             QueueCommand::NextBatch {
                 response_sender,
@@ -137,7 +138,8 @@ fn queue_blocking_task(
                     // Filter entries where the response receiver was dropped (== entries where the request
                     // was dropped by the client)
                     if entry.metadata.response_tx.is_closed() {
-                        metrics::increment_counter!("te_request_failure", "err" => "dropped");
+                        let counter = metrics::counter!("te_request_failure", "err" => "dropped");
+                        counter.increment(1);
                         continue;
                     }
 
@@ -197,9 +199,12 @@ fn queue_blocking_task(
 
                 let _ = response_sender.send(next_batch);
 
-                metrics::histogram!("te_batch_next_size", batch_size as f64);
-                metrics::histogram!("te_batch_next_tokens", current_tokens as f64);
-                metrics::gauge!("te_queue_size", entries.len() as f64);
+                let histogram = metrics::histogram!("te_batch_next_size");
+                histogram.record(batch_size as f64);
+                let histogram = metrics::histogram!("te_batch_next_tokens");
+                histogram.record(current_tokens as f64);
+                let gauge = metrics::gauge!("te_queue_size");
+                gauge.set(entries.len() as f64)
             }
         }
     }
diff --git a/core/src/tokenization.rs b/core/src/tokenization.rs
index 46f1411e..c33bfafc 100644
--- a/core/src/tokenization.rs
+++ b/core/src/tokenization.rs
@@ -1,16 +1,17 @@
 /// Payload tokenization logic
 use crate::TextEmbeddingsError;
+use std::collections::HashMap;
 use tokenizers::tokenizer::Tokenizer;
 pub use tokenizers::Encoding as RawEncoding;
 use tokenizers::{TruncationDirection, TruncationParams, TruncationStrategy};
-use tokio::sync::{mpsc, oneshot};
+use tokio::sync::oneshot;
 use tracing::{instrument, Span};
 
 /// Validation
 #[derive(Debug, Clone)]
 pub struct Tokenization {
     /// Channel to communicate with the background tokenization task
-    sender: mpsc::UnboundedSender<TokenizerRequest>,
+    sender: async_channel::Sender<TokenizerRequest>,
 }
 
 impl Tokenization {
@@ -19,43 +20,33 @@ impl Tokenization {
         tokenizer: Tokenizer,
         max_input_length: usize,
         position_offset: usize,
+        default_prompt: Option<String>,
+        prompts: Option<HashMap<String, String>>,
     ) -> Self {
         tracing::info!("Starting {workers} tokenization workers");
 
         // Create channel
-        let (sender, mut round_robin_receiver) = mpsc::unbounded_channel();
-        let mut senders = Vec::with_capacity(workers);
+        let (sender, receiver) = async_channel::bounded(workers * 4);
 
         // Create workers
         for _ in 0..workers {
             let tokenizer_clone = tokenizer.clone();
-            let (tokenizer_sender, tokenizer_receiver) = mpsc::unbounded_channel();
-            senders.push(tokenizer_sender);
-
+            let receiver_clone = receiver.clone();
+            let default_prompt_clone = default_prompt.clone();
+            let prompts_clone = prompts.clone();
             // Spawn worker
             std::thread::spawn(move || {
                 tokenizer_worker(
                     tokenizer_clone,
                     max_input_length,
                     position_offset,
-                    tokenizer_receiver,
+                    default_prompt_clone,
+                    prompts_clone,
+                    receiver_clone,
                 )
             });
         }
 
-        // Create tokenization round robin task
-        tokio::spawn(async move {
-            // Loop over requests
-            loop {
-                for sender in &senders {
-                    match round_robin_receiver.recv().await {
-                        None => return,
-                        Some(request) => sender.send(request).unwrap(),
-                    };
-                }
-            }
-        });
-
         Self { sender }
     }
 
@@ -64,6 +55,8 @@ impl Tokenization {
         &self,
         inputs: EncodingInput,
         truncate: bool,
+        truncation_direction: TruncationDirection,
+        prompt_name: Option<String>,
     ) -> Result<ValidEncoding, TextEmbeddingsError> {
         // Check if inputs is empty
         if inputs.is_empty() {
@@ -80,9 +73,12 @@ impl Tokenization {
             .send(TokenizerRequest::Encode(
                 inputs,
                 truncate,
+                truncation_direction,
+                prompt_name,
                 response_sender,
                 Span::current(),
             ))
+            .await
             .expect("Tokenization background task dropped the receiver. This is a bug.");
 
         // Await on response channel
@@ -95,7 +91,8 @@ impl Tokenization {
         &self,
         inputs: EncodingInput,
         add_special_tokens: bool,
-    ) -> Result<RawEncoding, TextEmbeddingsError> {
+        prompt_name: Option<String>,
+    ) -> Result<(Option<String>, RawEncoding), TextEmbeddingsError> {
         // Check if inputs is empty
         if inputs.is_empty() {
             return Err(TextEmbeddingsError::Validation(
@@ -111,9 +108,11 @@ impl Tokenization {
             .send(TokenizerRequest::Tokenize(
                 inputs,
                 add_special_tokens,
+                prompt_name,
                 response_sender,
                 Span::current(),
             ))
+            .await
             .expect("Tokenization background task dropped the receiver. This is a bug.");
 
         // Await on response channel
@@ -145,6 +144,7 @@ impl Tokenization {
                 response_sender,
                 Span::current(),
             ))
+            .await
             .expect("Tokenization background task dropped the receiver. This is a bug.");
 
         // Await on response channel
@@ -158,35 +158,67 @@ fn tokenizer_worker(
     mut tokenizer: Tokenizer,
     max_input_length: usize,
     position_offset: usize,
-    mut receiver: mpsc::UnboundedReceiver<TokenizerRequest>,
+    default_prompt: Option<String>,
+    prompts: Option<HashMap<String, String>>,
+    receiver: async_channel::Receiver<TokenizerRequest>,
 ) {
     // Loop over requests
-    while let Some(request) = receiver.blocking_recv() {
+    while let Ok(request) = receiver.recv_blocking() {
         match request {
-            TokenizerRequest::Encode(inputs, truncate, response_tx, parent_span) => {
+            TokenizerRequest::Encode(
+                inputs,
+                truncate,
+                truncation_direction,
+                prompt_name,
+                response_tx,
+                parent_span,
+            ) => {
                 parent_span.in_scope(|| {
                     if !response_tx.is_closed() {
+                        let default_prompt_clone = match prompt_name {
+                            None => default_prompt.clone(),
+                            Some(_) => None,
+                        };
+
                         // It's possible that the user dropped its request resulting in a send error.
                         // We just discard the error
                         let _ = response_tx.send(encode_input(
                             inputs,
                             truncate,
+                            truncation_direction,
                             max_input_length,
                             position_offset,
+                            default_prompt_clone,
+                            prompt_name,
+                            prompts.as_ref(),
                             &mut tokenizer,
                         ));
                     }
                 })
             }
-            TokenizerRequest::Tokenize(inputs, add_special_tokens, response_tx, parent_span) => {
+            TokenizerRequest::Tokenize(
+                inputs,
+                add_special_tokens,
+                prompt_name,
+                response_tx,
+                parent_span,
+            ) => {
                 parent_span.in_scope(|| {
                     if !response_tx.is_closed() {
+                        let default_prompt_clone = match prompt_name {
+                            None => default_prompt.clone(),
+                            Some(_) => None,
+                        };
+
                         // It's possible that the user dropped its request resulting in a send error.
                         // We just discard the error
                         let _ = response_tx.send(tokenize_input(
                             inputs,
                             add_special_tokens,
                             None,
+                            default_prompt_clone,
+                            prompt_name,
+                            prompts.as_ref(),
                             &mut tokenizer,
                         ));
                     }
@@ -216,50 +248,123 @@ fn decode_ids(
         .decode(&ids, skip_special_tokens)?)
 }
 
+fn prepare_pre_prompt(
+    default_prompt: Option<String>,
+    prompt_name: Option<String>,
+    prompts: Option<&HashMap<String, String>>,
+) -> Result<Option<String>, TextEmbeddingsError> {
+    let pre_prompt = if let Some(prompt_name) = prompt_name.as_ref() {
+        match prompts {
+            None => {
+                return Err(TextEmbeddingsError::Validation(format!("`default-prompt-name` is set to `{prompt_name}` but no prompts were found in the Sentence Transformers configuration")));
+            }
+            Some(prompts) if !prompts.contains_key(prompt_name) => {
+                return Err(TextEmbeddingsError::Validation(format!("`default-prompt-name` is set to `{prompt_name}` but it was not found in the Sentence Transformers prompts. Available prompts: {:?}", prompts.keys())));
+            }
+            Some(prompts) => prompts.get(prompt_name).cloned(),
+        }
+    } else {
+        default_prompt
+    };
+    Ok(pre_prompt)
+}
+
 fn tokenize_input(
     inputs: EncodingInput,
     add_special_tokens: bool,
     truncate_params: Option<TruncationParams>,
+    default_prompt: Option<String>,
+    prompt_name: Option<String>,
+    prompts: Option<&HashMap<String, String>>,
     tokenizer: &mut Tokenizer,
-) -> Result<RawEncoding, TextEmbeddingsError> {
+) -> Result<(Option<String>, RawEncoding), TextEmbeddingsError> {
+    let pre_prompt = prepare_pre_prompt(default_prompt, prompt_name, prompts)?;
+
     let encoding = match inputs {
         // encode input
-        EncodingInput::Single(s) => tokenizer
-            .with_truncation(truncate_params)?
-            .encode::<String>(s, add_special_tokens)?,
-        EncodingInput::Dual(s1, s2) => {
-            tokenizer
+        EncodingInput::Single(s) => {
+            let s = if let Some(mut pre_prompt) = pre_prompt {
+                pre_prompt.push_str(&s);
+                pre_prompt
+            } else {
+                s
+            };
+
+            let encoding = tokenizer
                 .with_truncation(truncate_params)?
-                .encode::<(String, String)>((s1, s2), add_special_tokens)?
+                .encode::<&str>(&s, add_special_tokens)?;
+
+            (Some(s), encoding)
+        }
+        EncodingInput::Dual(s1, s2) => {
+            if pre_prompt.is_some() {
+                return Err(TextEmbeddingsError::Validation(
+                    "`prompt_name` cannot be set with dual inputs".to_string(),
+                ));
+            }
+
+            (
+                None,
+                tokenizer
+                    .with_truncation(truncate_params)?
+                    .encode::<(String, String)>((s1, s2), add_special_tokens)?,
+            )
         }
         // input is encoded -> convert to tokenizers Encoding
         EncodingInput::Ids(ids) => {
-            let text = tokenizer.decode(&ids, false)?;
-            tokenizer
-                .with_truncation(truncate_params)?
-                .encode::<String>(text, false)?
+            if let Some(mut pre_prompt) = pre_prompt {
+                let text = tokenizer.decode(&ids, true)?;
+                pre_prompt.push_str(&text);
+
+                let encoding = tokenizer
+                    .with_truncation(truncate_params)?
+                    .encode::<&str>(&pre_prompt, true)?;
+
+                (Some(pre_prompt), encoding)
+            } else {
+                let text = tokenizer.decode(&ids, false)?;
+
+                let encoding = tokenizer
+                    .with_truncation(truncate_params)?
+                    .encode::<&str>(&text, false)?;
+
+                (Some(text), encoding)
+            }
         }
     };
     Ok(encoding)
 }
 
 /// Get input length and optionally truncate it
+#[allow(clippy::too_many_arguments)]
 fn encode_input(
     inputs: EncodingInput,
     truncate: bool,
+    truncation_direction: TruncationDirection,
     max_input_length: usize,
     position_offset: usize,
+    default_prompt: Option<String>,
+    prompt_name: Option<String>,
+    prompts: Option<&HashMap<String, String>>,
     tokenizer: &mut Tokenizer,
 ) -> Result<ValidEncoding, TextEmbeddingsError> {
     // Default truncation params
     let truncate_params = truncate.then_some(TruncationParams {
-        direction: TruncationDirection::Right,
+        direction: truncation_direction,
         max_length: max_input_length,
         strategy: TruncationStrategy::LongestFirst,
         stride: 0,
     });
 
-    let encoding = tokenize_input(inputs, true, truncate_params, tokenizer)?;
+    let (_, encoding) = tokenize_input(
+        inputs,
+        true,
+        truncate_params,
+        default_prompt,
+        prompt_name,
+        prompts,
+        tokenizer,
+    )?;
     let seq_len = encoding.len();
 
     if seq_len > max_input_length {
@@ -267,7 +372,8 @@ fn encode_input(
             "`inputs` must have less than {max_input_length} tokens. Given: {seq_len}"
         )));
     }
-    metrics::histogram!("te_request_input_length", seq_len as f64);
+    let histogram = metrics::histogram!("te_request_input_length");
+    histogram.record(seq_len as f64);
     Ok(ValidEncoding {
         input_ids: encoding.get_ids().to_vec(),
         token_type_ids: encoding.get_type_ids().to_vec(),
@@ -316,13 +422,16 @@ enum TokenizerRequest {
     Encode(
         EncodingInput,
         bool,
+        TruncationDirection,
+        Option<String>,
         oneshot::Sender<Result<ValidEncoding, TextEmbeddingsError>>,
         Span,
     ),
     Tokenize(
         EncodingInput,
         bool,
-        oneshot::Sender<Result<RawEncoding, TextEmbeddingsError>>,
+        Option<String>,
+        oneshot::Sender<Result<(Option<String>, RawEncoding), TextEmbeddingsError>>,
         Span,
     ),
     Decode(
diff --git a/docs/openapi.json b/docs/openapi.json
index 62c2d1c7..2c98ee6f 100644
--- a/docs/openapi.json
+++ b/docs/openapi.json
@@ -10,7 +10,7 @@
       "name": "Apache 2.0",
       "url": "https://www.apache.org/licenses/LICENSE-2.0"
     },
-    "version": "1.2."
+    "version": "1.4.0"
   },
   "paths": {
     "/decode": {
@@ -19,7 +19,6 @@
           "Text Embeddings Inference"
         ],
         "summary": "Decode input ids",
-        "description": "Decode input ids",
         "operationId": "decode",
         "requestBody": {
           "content": {
@@ -65,7 +64,6 @@
           "Text Embeddings Inference"
         ],
         "summary": "Get Embeddings. Returns a 424 status code if the model is not an embedding model.",
-        "description": "Get Embeddings. Returns a 424 status code if the model is not an embedding model.",
         "operationId": "embed",
         "requestBody": {
           "content": {
@@ -153,7 +151,7 @@
           "Text Embeddings Inference"
         ],
         "summary": "Get all Embeddings without Pooling.",
-        "description": "Get all Embeddings without Pooling.\nReturns a 424 status code if the model is not an embedding model.",
+        "description": "Returns a 424 status code if the model is not an embedding model.",
         "operationId": "embed_all",
         "requestBody": {
           "content": {
@@ -241,7 +239,6 @@
           "Text Embeddings Inference"
         ],
         "summary": "Get Sparse Embeddings. Returns a 424 status code if the model is not an embedding model with SPLADE pooling.",
-        "description": "Get Sparse Embeddings. Returns a 424 status code if the model is not an embedding model with SPLADE pooling.",
         "operationId": "embed_sparse",
         "requestBody": {
           "content": {
@@ -323,101 +320,12 @@
         }
       }
     },
-    "/embeddings": {
-      "post": {
-        "tags": [
-          "Text Embeddings Inference"
-        ],
-        "summary": "OpenAI compatible route. Returns a 424 status code if the model is not an embedding model.",
-        "description": "OpenAI compatible route. Returns a 424 status code if the model is not an embedding model.",
-        "operationId": "openai_embed",
-        "requestBody": {
-          "content": {
-            "application/json": {
-              "schema": {
-                "$ref": "#/components/schemas/OpenAICompatRequest"
-              }
-            }
-          },
-          "required": true
-        },
-        "responses": {
-          "200": {
-            "description": "Embeddings",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/OpenAICompatResponse"
-                }
-              }
-            }
-          },
-          "413": {
-            "description": "Batch size error",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
-                },
-                "example": {
-                  "message": "Batch size error",
-                  "type": "validation"
-                }
-              }
-            }
-          },
-          "422": {
-            "description": "Tokenization error",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
-                },
-                "example": {
-                  "message": "Tokenization error",
-                  "type": "tokenizer"
-                }
-              }
-            }
-          },
-          "424": {
-            "description": "Embedding Error",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
-                },
-                "example": {
-                  "message": "Inference failed",
-                  "type": "backend"
-                }
-              }
-            }
-          },
-          "429": {
-            "description": "Model is overloaded",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
-                },
-                "example": {
-                  "message": "Model is overloaded",
-                  "type": "overloaded"
-                }
-              }
-            }
-          }
-        }
-      }
-    },
     "/health": {
       "get": {
         "tags": [
           "Text Embeddings Inference"
         ],
         "summary": "Health check method",
-        "description": "Health check method",
         "operationId": "health",
         "responses": {
           "200": {
@@ -446,7 +354,6 @@
           "Text Embeddings Inference"
         ],
         "summary": "Text Embeddings Inference endpoint info",
-        "description": "Text Embeddings Inference endpoint info",
         "operationId": "get_model_info",
         "responses": {
           "200": {
@@ -468,7 +375,6 @@
           "Text Embeddings Inference"
         ],
         "summary": "Prometheus metrics scrape endpoint",
-        "description": "Prometheus metrics scrape endpoint",
         "operationId": "metrics",
         "responses": {
           "200": {
@@ -490,7 +396,6 @@
           "Text Embeddings Inference"
         ],
         "summary": "Get Predictions. Returns a 424 status code if the model is not a Sequence Classification model",
-        "description": "Get Predictions. Returns a 424 status code if the model is not a Sequence Classification model",
         "operationId": "predict",
         "requestBody": {
           "content": {
@@ -578,7 +483,7 @@
           "Text Embeddings Inference"
         ],
         "summary": "Get Ranks. Returns a 424 status code if the model is not a Sequence Classification model with",
-        "description": "Get Ranks. Returns a 424 status code if the model is not a Sequence Classification model with\na single class.",
+        "description": "a single class.",
         "operationId": "rerank",
         "requestBody": {
           "content": {
@@ -666,7 +571,6 @@
           "Text Embeddings Inference"
         ],
         "summary": "Tokenize inputs",
-        "description": "Tokenize inputs",
         "operationId": "tokenize",
         "requestBody": {
           "content": {
@@ -706,6 +610,174 @@
         }
       }
     },
+    "/v1/embeddings": {
+      "post": {
+        "tags": [
+          "Text Embeddings Inference"
+        ],
+        "summary": "OpenAI compatible route. Returns a 424 status code if the model is not an embedding model.",
+        "operationId": "openai_embed",
+        "requestBody": {
+          "content": {
+            "application/json": {
+              "schema": {
+                "$ref": "#/components/schemas/OpenAICompatRequest"
+              }
+            }
+          },
+          "required": true
+        },
+        "responses": {
+          "200": {
+            "description": "Embeddings",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/OpenAICompatResponse"
+                }
+              }
+            }
+          },
+          "413": {
+            "description": "Batch size error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
+                },
+                "example": {
+                  "message": "Batch size error",
+                  "type": "validation"
+                }
+              }
+            }
+          },
+          "422": {
+            "description": "Tokenization error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
+                },
+                "example": {
+                  "message": "Tokenization error",
+                  "type": "tokenizer"
+                }
+              }
+            }
+          },
+          "424": {
+            "description": "Embedding Error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
+                },
+                "example": {
+                  "message": "Inference failed",
+                  "type": "backend"
+                }
+              }
+            }
+          },
+          "429": {
+            "description": "Model is overloaded",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/OpenAICompatErrorResponse"
+                },
+                "example": {
+                  "message": "Model is overloaded",
+                  "type": "overloaded"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
+    "/vertex": {
+      "post": {
+        "tags": [
+          "Text Embeddings Inference"
+        ],
+        "summary": "Generate embeddings from a Vertex request",
+        "description": "Generate embeddings from a Vertex request",
+        "operationId": "vertex_compatibility",
+        "requestBody": {
+          "content": {
+            "application/json": {
+              "schema": {
+                "$ref": "#/components/schemas/VertexRequest"
+              }
+            }
+          },
+          "required": true
+        },
+        "responses": {
+          "200": {
+            "description": "Results"
+          },
+          "413": {
+            "description": "Batch size error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Batch size error",
+                  "error_type": "validation"
+                }
+              }
+            }
+          },
+          "422": {
+            "description": "Tokenization error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Tokenization error",
+                  "error_type": "tokenizer"
+                }
+              }
+            }
+          },
+          "424": {
+            "description": "Error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Inference failed",
+                  "error_type": "backend"
+                }
+              }
+            }
+          },
+          "429": {
+            "description": "Model is overloaded",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "Model is overloaded",
+                  "error_type": "overloaded"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
     "/vertex": {
       "post": {
         "tags": [
@@ -852,10 +924,26 @@
           "inputs": {
             "$ref": "#/components/schemas/Input"
           },
+          "prompt_name": {
+            "type": "string",
+            "description": "The name of the prompt that should be used by for encoding. If not set, no prompt\nwill be applied.\n\nMust be a key in the `Sentence Transformers` configuration `prompts` dictionary.\n\nFor example if ``prompt_name`` is \"query\" and the ``prompts`` is {\"query\": \"query: \", ...},\nthen the sentence \"What is the capital of France?\" will be encoded as\n\"query: What is the capital of France?\" because the prompt text will be prepended before\nany text to encode.",
+            "default": "null",
+            "example": "null",
+            "nullable": true
+          },
           "truncate": {
             "type": "boolean",
             "default": "false",
-            "example": "false"
+            "example": "false",
+            "nullable": true
+          },
+          "truncation_direction": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/TruncationDirection"
+              }
+            ],
+            "default": "right"
           }
         }
       },
@@ -895,10 +983,26 @@
             "default": "true",
             "example": "true"
           },
+          "prompt_name": {
+            "type": "string",
+            "description": "The name of the prompt that should be used by for encoding. If not set, no prompt\nwill be applied.\n\nMust be a key in the `Sentence Transformers` configuration `prompts` dictionary.\n\nFor example if ``prompt_name`` is \"query\" and the ``prompts`` is {\"query\": \"query: \", ...},\nthen the sentence \"What is the capital of France?\" will be encoded as\n\"query: What is the capital of France?\" because the prompt text will be prepended before\nany text to encode.",
+            "default": "null",
+            "example": "null",
+            "nullable": true
+          },
           "truncate": {
             "type": "boolean",
             "default": "false",
-            "example": "false"
+            "example": "false",
+            "nullable": true
+          },
+          "truncation_direction": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/TruncationDirection"
+              }
+            ],
+            "default": "right"
           }
         }
       },
@@ -928,10 +1032,26 @@
           "inputs": {
             "$ref": "#/components/schemas/Input"
           },
+          "prompt_name": {
+            "type": "string",
+            "description": "The name of the prompt that should be used by for encoding. If not set, no prompt\nwill be applied.\n\nMust be a key in the `Sentence Transformers` configuration `prompts` dictionary.\n\nFor example if ``prompt_name`` is \"query\" and the ``prompts`` is {\"query\": \"query: \", ...},\nthen the sentence \"What is the capital of France?\" will be encoded as\n\"query: What is the capital of France?\" because the prompt text will be prepended before\nany text to encode.",
+            "default": "null",
+            "example": "null",
+            "nullable": true
+          },
           "truncate": {
             "type": "boolean",
             "default": "false",
-            "example": "false"
+            "example": "false",
+            "nullable": true
+          },
+          "truncation_direction": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/TruncationDirection"
+              }
+            ],
+            "default": "right"
           }
         }
       },
@@ -944,6 +1064,20 @@
           }
         }
       },
+      "Embedding": {
+        "oneOf": [
+          {
+            "type": "array",
+            "items": {
+              "type": "number",
+              "format": "float"
+            }
+          },
+          {
+            "type": "string"
+          }
+        ]
+      },
       "EmbeddingModel": {
         "type": "object",
         "required": [
@@ -956,6 +1090,13 @@
           }
         }
       },
+      "EncodingFormat": {
+        "type": "string",
+        "enum": [
+          "float",
+          "base64"
+        ]
+      },
       "ErrorResponse": {
         "type": "object",
         "required": [
@@ -991,10 +1132,14 @@
           "max_input_length",
           "max_batch_tokens",
           "max_client_batch_size",
+          "auto_truncate",
           "tokenization_workers",
           "version"
         ],
         "properties": {
+          "auto_truncate": {
+            "type": "boolean"
+          },
           "docker_label": {
             "type": "string",
             "example": "null",
@@ -1063,6 +1208,42 @@
         }
       },
       "Input": {
+        "oneOf": [
+          {
+            "$ref": "#/components/schemas/InputType"
+          },
+          {
+            "type": "array",
+            "items": {
+              "$ref": "#/components/schemas/InputType"
+            }
+          }
+        ]
+      },
+      "InputIds": {
+        "oneOf": [
+          {
+            "type": "array",
+            "items": {
+              "type": "integer",
+              "format": "int32",
+              "minimum": 0
+            }
+          },
+          {
+            "type": "array",
+            "items": {
+              "type": "array",
+              "items": {
+                "type": "integer",
+                "format": "int32",
+                "minimum": 0
+              }
+            }
+          }
+        ]
+      },
+      "InputType": {
         "oneOf": [
           {
             "type": "string"
@@ -1070,7 +1251,32 @@
           {
             "type": "array",
             "items": {
-              "type": "string"
+              "type": "integer",
+              "format": "int32",
+              "minimum": 0
+            }
+          }
+        ]
+      },
+      "InputIds": {
+        "oneOf": [
+          {
+            "type": "array",
+            "items": {
+              "type": "integer",
+              "format": "int32",
+              "minimum": 0
+            }
+          },
+          {
+            "type": "array",
+            "items": {
+              "type": "array",
+              "items": {
+                "type": "integer",
+                "format": "int32",
+                "minimum": 0
+              }
             }
           }
         ]
@@ -1144,16 +1350,7 @@
         ],
         "properties": {
           "embedding": {
-            "type": "array",
-            "items": {
-              "type": "number",
-              "format": "float"
-            },
-            "example": [
-              0.0,
-              1.0,
-              2.0
-            ]
+            "$ref": "#/components/schemas/Embedding"
           },
           "index": {
             "type": "integer",
@@ -1193,6 +1390,14 @@
           "input"
         ],
         "properties": {
+          "encoding_format": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/EncodingFormat"
+              }
+            ],
+            "default": "float"
+          },
           "input": {
             "$ref": "#/components/schemas/Input"
           },
@@ -1317,7 +1522,16 @@
           "truncate": {
             "type": "boolean",
             "default": "false",
-            "example": "false"
+            "example": "false",
+            "nullable": true
+          },
+          "truncation_direction": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/TruncationDirection"
+              }
+            ],
+            "default": "right"
           }
         }
       },
@@ -1416,7 +1630,16 @@
           "truncate": {
             "type": "boolean",
             "default": "false",
-            "example": "false"
+            "example": "false",
+            "nullable": true
+          },
+          "truncation_direction": {
+            "allOf": [
+              {
+                "$ref": "#/components/schemas/TruncationDirection"
+              }
+            ],
+            "default": "right"
           }
         }
       },
@@ -1479,6 +1702,19 @@
           }
         }
       },
+      "TokenizeInput": {
+        "oneOf": [
+          {
+            "type": "string"
+          },
+          {
+            "type": "array",
+            "items": {
+              "type": "string"
+            }
+          }
+        ]
+      },
       "TokenizeRequest": {
         "type": "object",
         "required": [
@@ -1491,7 +1727,14 @@
             "example": "true"
           },
           "inputs": {
-            "$ref": "#/components/schemas/Input"
+            "$ref": "#/components/schemas/TokenizeInput"
+          },
+          "prompt_name": {
+            "type": "string",
+            "description": "The name of the prompt that should be used by for encoding. If not set, no prompt\nwill be applied.\n\nMust be a key in the `Sentence Transformers` configuration `prompts` dictionary.\n\nFor example if ``prompt_name`` is \"query\" and the ``prompts`` is {\"query\": \"query: \", ...},\nthen the sentence \"What is the capital of France?\" will be encoded as\n\"query: What is the capital of France?\" because the prompt text will be prepended before\nany text to encode.",
+            "default": "null",
+            "example": "null",
+            "nullable": true
           }
         }
       },
@@ -1515,273 +1758,12 @@
           ]
         ]
       },
-      "VertexInstance": {
-        "oneOf": [
-          {
-            "allOf": [
-              {
-                "$ref": "#/components/schemas/EmbedRequest"
-              },
-              {
-                "type": "object",
-                "required": [
-                  "type"
-                ],
-                "properties": {
-                  "type": {
-                    "type": "string",
-                    "enum": [
-                      "embed"
-                    ]
-                  }
-                }
-              }
-            ]
-          },
-          {
-            "allOf": [
-              {
-                "$ref": "#/components/schemas/EmbedAllRequest"
-              },
-              {
-                "type": "object",
-                "required": [
-                  "type"
-                ],
-                "properties": {
-                  "type": {
-                    "type": "string",
-                    "enum": [
-                      "embed_all"
-                    ]
-                  }
-                }
-              }
-            ]
-          },
-          {
-            "allOf": [
-              {
-                "$ref": "#/components/schemas/EmbedSparseRequest"
-              },
-              {
-                "type": "object",
-                "required": [
-                  "type"
-                ],
-                "properties": {
-                  "type": {
-                    "type": "string",
-                    "enum": [
-                      "embed_sparse"
-                    ]
-                  }
-                }
-              }
-            ]
-          },
-          {
-            "allOf": [
-              {
-                "$ref": "#/components/schemas/PredictRequest"
-              },
-              {
-                "type": "object",
-                "required": [
-                  "type"
-                ],
-                "properties": {
-                  "type": {
-                    "type": "string",
-                    "enum": [
-                      "predict"
-                    ]
-                  }
-                }
-              }
-            ]
-          },
-          {
-            "allOf": [
-              {
-                "$ref": "#/components/schemas/RerankRequest"
-              },
-              {
-                "type": "object",
-                "required": [
-                  "type"
-                ],
-                "properties": {
-                  "type": {
-                    "type": "string",
-                    "enum": [
-                      "rerank"
-                    ]
-                  }
-                }
-              }
-            ]
-          },
-          {
-            "allOf": [
-              {
-                "$ref": "#/components/schemas/TokenizeRequest"
-              },
-              {
-                "type": "object",
-                "required": [
-                  "type"
-                ],
-                "properties": {
-                  "type": {
-                    "type": "string",
-                    "enum": [
-                      "tokenize"
-                    ]
-                  }
-                }
-              }
-            ]
-          }
-        ],
-        "discriminator": {
-          "propertyName": "type"
-        }
-      },
-      "VertexRequest": {
-        "type": "object",
-        "required": [
-          "instances"
-        ],
-        "properties": {
-          "instances": {
-            "type": "array",
-            "items": {
-              "$ref": "#/components/schemas/VertexInstance"
-            }
-          }
-        }
-      },
-      "VertexResponse": {
-        "type": "array",
-        "items": {
-          "$ref": "#/components/schemas/VertexResponseInstance"
-        }
-      },
-      "VertexResponseInstance": {
-        "oneOf": [
-          {
-            "type": "object",
-            "required": [
-              "type",
-              "result"
-            ],
-            "properties": {
-              "result": {
-                "$ref": "#/components/schemas/EmbedResponse"
-              },
-              "type": {
-                "type": "string",
-                "enum": [
-                  "embed"
-                ]
-              }
-            }
-          },
-          {
-            "type": "object",
-            "required": [
-              "type",
-              "result"
-            ],
-            "properties": {
-              "result": {
-                "$ref": "#/components/schemas/EmbedAllResponse"
-              },
-              "type": {
-                "type": "string",
-                "enum": [
-                  "embed_all"
-                ]
-              }
-            }
-          },
-          {
-            "type": "object",
-            "required": [
-              "type",
-              "result"
-            ],
-            "properties": {
-              "result": {
-                "$ref": "#/components/schemas/EmbedSparseResponse"
-              },
-              "type": {
-                "type": "string",
-                "enum": [
-                  "embed_sparse"
-                ]
-              }
-            }
-          },
-          {
-            "type": "object",
-            "required": [
-              "type",
-              "result"
-            ],
-            "properties": {
-              "result": {
-                "$ref": "#/components/schemas/PredictResponse"
-              },
-              "type": {
-                "type": "string",
-                "enum": [
-                  "predict"
-                ]
-              }
-            }
-          },
-          {
-            "type": "object",
-            "required": [
-              "type",
-              "result"
-            ],
-            "properties": {
-              "result": {
-                "$ref": "#/components/schemas/RerankResponse"
-              },
-              "type": {
-                "type": "string",
-                "enum": [
-                  "rerank"
-                ]
-              }
-            }
-          },
-          {
-            "type": "object",
-            "required": [
-              "type",
-              "result"
-            ],
-            "properties": {
-              "result": {
-                "$ref": "#/components/schemas/TokenizeResponse"
-              },
-              "type": {
-                "type": "string",
-                "enum": [
-                  "tokenize"
-                ]
-              }
-            }
-          }
-        ],
-        "discriminator": {
-          "propertyName": "type"
-        }
+      "TruncationDirection": {
+        "type": "string",
+        "enum": [
+          "Left",
+          "Right"
+        ]
       }
     }
   },
diff --git a/docs/source/en/cli_arguments.md b/docs/source/en/cli_arguments.md
index c79b7f9c..93e42320 100644
--- a/docs/source/en/cli_arguments.md
+++ b/docs/source/en/cli_arguments.md
@@ -62,9 +62,11 @@ Options:
           [env: POOLING=]
 
           Possible values:
-          - cls:    Select the CLS token as embedding
-          - mean:   Apply Mean pooling to the model embeddings
-          - splade: Apply SPLADE (Sparse Lexical and Expansion) to the model embeddings. This option is only available if the loaded model is a `ForMaskedLM` Transformer model
+          - cls:        Select the CLS token as embedding
+          - mean:       Apply Mean pooling to the model embeddings
+          - splade:     Apply SPLADE (Sparse Lexical and Expansion) to the model embeddings. This option is only
+          available if the loaded model is a `ForMaskedLM` Transformer model
+          - last-token: Select the last token as embedding
 
       --max-concurrent-requests <MAX_CONCURRENT_REQUESTS>
           The maximum amount of concurrent requests for this particular deployment.
@@ -98,6 +100,37 @@ Options:
           [env: MAX_CLIENT_BATCH_SIZE=]
           [default: 32]
 
+      --auto-truncate
+          Automatically truncate inputs that are longer than the maximum supported size
+
+          Unused for gRPC servers
+
+          [env: AUTO_TRUNCATE=]
+
+      --default-prompt-name <DEFAULT_PROMPT_NAME>
+          The name of the prompt that should be used by default for encoding. If not set, no prompt will be applied.
+
+          Must be a key in the `Sentence Transformers` configuration `prompts` dictionary.
+
+          For example if ``default_prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...}, then the
+          sentence "What is the capital of France?" will be encoded as "query: What is the capital of France?" because
+          the prompt text will be prepended before any text to encode.
+
+          The argument '--default-prompt-name <DEFAULT_PROMPT_NAME>' cannot be used with '--default-prompt <DEFAULT_PROMPT>`
+
+          [env: DEFAULT_PROMPT_NAME=]
+
+      --default-prompt <DEFAULT_PROMPT>
+          The prompt that should be used by default for encoding. If not set, no prompt will be applied.
+
+          For example if ``default_prompt`` is "query: " then the sentence "What is the capital of France?" will be
+          encoded as "query: What is the capital of France?" because the prompt text will be prepended before any text
+          to encode.
+
+          The argument '--default-prompt <DEFAULT_PROMPT>' cannot be used with '--default-prompt-name <DEFAULT_PROMPT_NAME>`
+
+          [env: DEFAULT_PROMPT=]
+
       --hf-api-token <HF_API_TOKEN>
           Your HuggingFace hub token
 
@@ -126,7 +159,38 @@ Options:
           The location of the huggingface hub cache. Used to override the location if you want to provide a mounted disk
           for instance
 
-          [env: HUGGINGFACE_HUB_CACHE=/data]
+          [env: HUGGINGFACE_HUB_CACHE=]
+
+      --payload-limit <PAYLOAD_LIMIT>
+          Payload size limit in bytes
+
+          Default is 2MB
+
+          [env: PAYLOAD_LIMIT=]
+          [default: 2000000]
+
+      --api-key <API_KEY>
+          Set an api key for request authorization.
+
+          By default the server responds to every request. With an api key set, the requests must have the Authorization
+          header set with the api key as Bearer token.
+
+          [env: API_KEY=]
+
+      --payload-limit <PAYLOAD_LIMIT>
+          Payload size limit in bytes
+
+          Default is 2MB
+
+          [env: PAYLOAD_LIMIT=]
+          [default: 2000000]
+
+      --api-key <API_KEY>
+          Set an api key for request authorization.
+
+          By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token.
+
+          [env: API_KEY=]
 
       --payload-limit <PAYLOAD_LIMIT>
           Payload size limit in bytes
@@ -153,6 +217,14 @@ Options:
 
           [env: OTLP_ENDPOINT=]
 
+      --otlp-service-name <OTLP_SERVICE_NAME>
+          The service name for opentelemetry. e.g. `text-embeddings-inference.server`
+
+          [env: OTLP_SERVICE_NAME=]
+          [default: text-embeddings-inference.server]
+
       --cors-allow-origin <CORS_ALLOW_ORIGIN>
+          Unused for gRPC servers
+
           [env: CORS_ALLOW_ORIGIN=]
 ```
diff --git a/docs/source/en/private_models.md b/docs/source/en/private_models.md
index bd9041c7..b5ae060d 100644
--- a/docs/source/en/private_models.md
+++ b/docs/source/en/private_models.md
@@ -24,10 +24,10 @@ Once you have confirmed that you have access to the model:
 - Navigate to your account's [Profile | Settings | Access Tokens page](https://huggingface.co/settings/tokens).
 - Generate and copy a read token.
 
-If you're the CLI, set the `HUGGING_FACE_HUB_TOKEN` environment variable. For example:
+If you're the CLI, set the `HF_API_TOKEN` environment variable. For example:
 
 ```shell
-export HUGGING_FACE_HUB_TOKEN=<YOUR READ TOKEN>
+export HF_API_TOKEN=<YOUR READ TOKEN>
 ```
 
 Alternatively, you can provide the token when deploying the model with Docker:
@@ -37,5 +37,5 @@ model=<your private model>
 volume=$PWD/data
 token=<your cli Hugging Face Hub token>
 
-docker run --gpus all -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model
+docker run --gpus all -e HF_API_TOKEN=$token -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.4 --model-id $model
 ```
diff --git a/docs/source/en/quick_tour.md b/docs/source/en/quick_tour.md
index c0fe008c..b557f267 100644
--- a/docs/source/en/quick_tour.md
+++ b/docs/source/en/quick_tour.md
@@ -34,7 +34,7 @@ model=BAAI/bge-large-en-v1.5
 revision=refs/pr/5
 volume=$PWD/data
 
-docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model --revision $revision
+docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.4 --model-id $model --revision $revision
 ```
 
 <Tip>
@@ -69,7 +69,7 @@ model=BAAI/bge-reranker-large
 revision=refs/pr/4
 volume=$PWD/data
 
-docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model --revision $revision
+docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.4 --model-id $model --revision $revision
 ```
 
 Once you have deployed a model, you can use the `rerank` endpoint to rank the similarity between a query and a list
@@ -90,7 +90,7 @@ You can also use classic Sequence Classification models like `SamLowe/roberta-ba
 model=SamLowe/roberta-base-go_emotions
 volume=$PWD/data
 
-docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id $model
+docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.4 --model-id $model
 ```
 
 Once you have deployed the model you can use the `predict` endpoint to get the emotions most associated with an input:
diff --git a/docs/source/en/supported_models.md b/docs/source/en/supported_models.md
index 64ecbc1f..aee56971 100644
--- a/docs/source/en/supported_models.md
+++ b/docs/source/en/supported_models.md
@@ -20,18 +20,21 @@ We are continually expanding our support for other model types and plan to inclu
 
 ## Supported embeddings models
 
-Text Embeddings Inference currently supports BERT, CamemBERT, XLM-RoBERTa models with absolute positions and JinaBERT
-model with Alibi positions.
+Text Embeddings Inference currently supports Nomic, BERT, CamemBERT, XLM-RoBERTa models with absolute positions, JinaBERT
+model with Alibi positions and Mistral, Alibaba GTE and Qwen2 models with Rope positions.
 
 Below are some examples of the currently supported models:
 
-| MTEB Rank | Model Type  | Model ID                                                                                         |
-|-----------|-------------|--------------------------------------------------------------------------------------------------|
-| 6         | Bert        | [WhereIsAI/UAE-Large-V1](https://hf.co/WhereIsAI/UAE-Large-V1)                                   |
-| 10        | XLM-RoBERTa | [intfloat/multilingual-e5-large-instruct](https://hf.co/intfloat/multilingual-e5-large-instruct) |
-| N/A       | NomicBert   | [nomic-ai/nomic-embed-text-v1](https://hf.co/nomic-ai/nomic-embed-text-v1)                       |
-| N/A       | NomicBert   | [nomic-ai/nomic-embed-text-v1.5](https://hf.co/nomic-ai/nomic-embed-text-v1.5)                   |
-| N/A       | JinaBERT    | [jinaai/jina-embeddings-v2-base-en](https://hf.co/jinaai/jina-embeddings-v2-base-en)             |
+| MTEB Rank | Model Size     | Model Type  | Model ID                                                                                         |
+|-----------|----------------|-------------|--------------------------------------------------------------------------------------------------|
+| 1         | 7B (Very Slow) | Mistral     | [Salesforce/SFR-Embedding-2_R](https://hf.co/Salesforce/SFR-Embedding-2_R)                       |
+| 15        | 0.4B           | Alibaba GTE | [Alibaba-NLP/gte-large-en-v1.5](Alibaba-NLP/gte-large-en-v1.5)                                   |
+| 20        | 0.3B           | Bert        | [WhereIsAI/UAE-Large-V1](https://hf.co/WhereIsAI/UAE-Large-V1)                                   |
+| 24        | 0.5B           | XLM-RoBERTa | [intfloat/multilingual-e5-large-instruct](https://hf.co/intfloat/multilingual-e5-large-instruct) |
+| N/A       | 0.1B           | NomicBert   | [nomic-ai/nomic-embed-text-v1](https://hf.co/nomic-ai/nomic-embed-text-v1)                       |
+| N/A       | 0.1B           | NomicBert   | [nomic-ai/nomic-embed-text-v1.5](https://hf.co/nomic-ai/nomic-embed-text-v1.5)                   |
+| N/A       | 0.1B           | JinaBERT    | [jinaai/jina-embeddings-v2-base-en](https://hf.co/jinaai/jina-embeddings-v2-base-en)             |
+| N/A       | 0.1B           | JinaBERT    | [jinaai/jina-embeddings-v2-base-code](https://hf.co/jinaai/jina-embeddings-v2-base-code)         |
 
 
 To explore the list of best performing text embeddings models, visit the
@@ -63,13 +66,13 @@ Find the appropriate Docker image for your hardware in the following table:
 
 | Architecture                        | Image                                                                    |
 |-------------------------------------|--------------------------------------------------------------------------|
-| CPU                                 | ghcr.io/huggingface/text-embeddings-inference:cpu-1.2                    |
+| CPU                                 | ghcr.io/huggingface/text-embeddings-inference:cpu-1.4                    |
 | Volta                               | NOT SUPPORTED                                                            |
-| Turing (T4, RTX 2000 series, ...)   | ghcr.io/huggingface/text-embeddings-inference:turing-1.2 (experimental)  |
-| Ampere 80 (A100, A30)               | ghcr.io/huggingface/text-embeddings-inference:1.2                        |
-| Ampere 86 (A10, A40, ...)           | ghcr.io/huggingface/text-embeddings-inference:86-1.2                     |
-| Ada Lovelace (RTX 4000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:89-1.2                     |
-| Hopper (H100)                       | ghcr.io/huggingface/text-embeddings-inference:hopper-1.2 (experimental)  |
+| Turing (T4, RTX 2000 series, ...)   | ghcr.io/huggingface/text-embeddings-inference:turing-1.4 (experimental)  |
+| Ampere 80 (A100, A30)               | ghcr.io/huggingface/text-embeddings-inference:1.4                        |
+| Ampere 86 (A10, A40, ...)           | ghcr.io/huggingface/text-embeddings-inference:86-1.4                     |
+| Ada Lovelace (RTX 4000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:89-1.4                     |
+| Hopper (H100)                       | ghcr.io/huggingface/text-embeddings-inference:hopper-1.4 (experimental)  |
 
 **Warning**: Flash Attention is turned off by default for the Turing image as it suffers from precision issues.
 You can turn Flash Attention v1 ON by using the `USE_FLASH_ATTENTION=True` environment variable.
diff --git a/load_tests/load.js b/load_tests/load.js
index b7e4524e..b3705476 100644
--- a/load_tests/load.js
+++ b/load_tests/load.js
@@ -2,7 +2,7 @@ import {check} from 'k6';
 import http from 'k6/http';
 import {Trend} from 'k6/metrics';
 
-const host = __ENV.HOST || '127.0.0.1:8080';
+const host = __ENV.HOST || '127.0.0.1:3000';
 
 const totalTime = new Trend('total_time', true);
 const tokenizationTIme = new Trend('tokenization_time', true);
@@ -27,7 +27,7 @@ export const options = {
             executor: 'constant-arrival-rate',
             duration: '30s',
             preAllocatedVUs: 5000,
-            rate: 1000,
+            rate: 10,
             timeUnit: '1s',
             gracefulStop: '1s',
         },
@@ -36,8 +36,9 @@ export const options = {
 
 export default function () {
     const payload = JSON.stringify({
-        query: inputs,
-        texts: [inputs],
+        inputs: inputs,
+        // query: inputs,
+        // texts: [inputs],
         truncate: true,
     });
 
diff --git a/load_tests/load_grpc.js b/load_tests/load_grpc.js
index bce6834e..30d2676a 100644
--- a/load_tests/load_grpc.js
+++ b/load_tests/load_grpc.js
@@ -2,7 +2,7 @@ import {check} from 'k6';
 import grpc from 'k6/experimental/grpc';
 import {Trend} from 'k6/metrics';
 
-const host = __ENV.HOST || '127.0.0.1:8080';
+const host = __ENV.HOST || '127.0.0.1:3000';
 
 const totalTime = new Trend('total_time', true);
 const tokenizationTIme = new Trend('tokenization_time', true);
@@ -25,7 +25,7 @@ export const options = {
         // },
         load_test: {
             executor: 'constant-arrival-rate',
-            duration: '30s',
+            duration: '5m',
             preAllocatedVUs: 5000,
             rate: 1000,
             timeUnit: '1s',
diff --git a/proto/tei.proto b/proto/tei.proto
index 6538e34a..aac6c2ba 100644
--- a/proto/tei.proto
+++ b/proto/tei.proto
@@ -69,10 +69,17 @@ message Metadata {
     uint64 inference_time_ns = 6;
 }
 
+enum TruncationDirection {
+    TRUNCATION_DIRECTION_RIGHT = 0;
+    TRUNCATION_DIRECTION_LEFT = 1;
+}
+
 message EmbedRequest {
     string inputs = 1;
     bool truncate = 2;
     bool normalize = 3;
+    TruncationDirection truncation_direction = 4;
+    optional string prompt_name = 5;
 }
 
 message EmbedResponse {
@@ -83,6 +90,8 @@ message EmbedResponse {
 message EmbedSparseRequest {
     string inputs = 1;
     bool truncate = 2;
+    TruncationDirection truncation_direction = 3;
+    optional string prompt_name = 4;
 }
 
 message SparseValue {
@@ -98,6 +107,8 @@ message EmbedSparseResponse {
 message EmbedAllRequest {
     string inputs = 1;
     bool truncate = 2;
+    TruncationDirection truncation_direction = 3;
+    optional string prompt_name = 4;
 }
 
 message TokenEmbedding {
@@ -113,12 +124,14 @@ message PredictRequest {
     string inputs = 1;
     bool truncate = 2;
     bool raw_scores = 3;
+    TruncationDirection truncation_direction = 4;
 }
 
 message PredictPairRequest {
     repeated string inputs = 1;
     bool truncate = 2;
     bool raw_scores = 3;
+    TruncationDirection truncation_direction = 4;
 }
 
 message Prediction {
@@ -137,6 +150,7 @@ message RerankRequest {
     bool truncate = 3;
     bool raw_scores = 4;
     bool return_text = 5;
+    TruncationDirection truncation_direction = 6;
 }
 
 message RerankStreamRequest{
@@ -147,6 +161,7 @@ message RerankStreamRequest{
     bool raw_scores = 4;
     // The server will only consider the first value
     bool return_text = 5;
+    TruncationDirection truncation_direction = 6;
 }
 
 message Rank {
@@ -163,6 +178,7 @@ message RerankResponse {
 message EncodeRequest {
     string inputs = 1;
     bool add_special_tokens = 2;
+    optional string prompt_name = 3;
 }
 
 message SimpleToken {
diff --git a/router/Cargo.toml b/router/Cargo.toml
index ae10065c..44e56015 100644
--- a/router/Cargo.toml
+++ b/router/Cargo.toml
@@ -15,37 +15,38 @@ name = "text-embeddings-router"
 path = "src/main.rs"
 
 [dependencies]
-anyhow = "1.0.71"
+anyhow = { workspace = true }
 text-embeddings-backend = { path = "../backends", features = ["clap"] }
 text-embeddings-core = { path = "../core" }
-clap = { version = "4.1.4", features = ["derive", "env"] }
+clap = { workspace = true }
 futures = "^0.3"
-init-tracing-opentelemetry = { version = "0.14.1", features = ["opentelemetry-otlp"] }
-hf-hub = { version = "0.3.0", features = ["tokio"] }
+init-tracing-opentelemetry = { version = "0.18.1", features = ["opentelemetry-otlp"] }
+hf-hub = { workspace = true }
 http = "1.0.0"
 num_cpus = "1.16.0"
-metrics = "0.21.0"
-metrics-exporter-prometheus = { version = "0.12.1", features = [] }
-opentelemetry = { version = "0.20.0", features = ["rt-tokio"] }
-opentelemetry-otlp = "0.13.0"
-reqwest = { version = "0.11.14", features = [] }
-serde = "1.0.152"
-serde_json = "1.0.93"
-thiserror = "1.0.38"
-tokenizers = { version = "0.15.2", default-features=false, features=["onig", "esaxx_fast"] }
-tokio = { version = "1.25.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync"] }
-tracing = "0.1.37"
-tracing-opentelemetry = "0.21.0"
+metrics = { workspace = true }
+metrics-exporter-prometheus = { version = "0.15.1", features = [] }
+opentelemetry = "0.23.0"
+opentelemetry_sdk = { version = "0.23.0", features = ["rt-tokio"] }
+opentelemetry-otlp = "0.16.0"
+reqwest = { version = "0.12.5", features = [] }
+serde = { workspace = true }
+serde_json = { workspace = true }
+thiserror = { workspace = true }
+tokenizers = { workspace = true }
+tokio = { workspace = true }
+tracing = { workspace = true }
+tracing-opentelemetry = "0.24.0"
 tracing-subscriber = { version = "0.3.16", features = ["json", "env-filter"] }
 veil = "0.1.6"
-mimalloc = { version = "*", default-features = false }
 
 # HTTP dependencies
 axum = { version = "0.7.4", features = ["json"], optional = true }
-axum-tracing-opentelemetry = { version = "0.17.0", optional = true }
+axum-tracing-opentelemetry = { version = "0.18.1", optional = true }
+base64 = { version = "0.22.1", optional = true }
 tower-http = { version = "0.5.1", features = ["cors"], optional = true }
 utoipa = { version = "4.2", features = ["axum_extras"], optional = true }
-utoipa-swagger-ui = { version = "6.0", features = ["axum"], optional = true }
+utoipa-swagger-ui = { version = "7.1", features = ["axum"], optional = true }
 
 # gRPC dependencies
 async-stream = { version = "0.3.5", optional = true }
@@ -55,18 +56,25 @@ tonic-health = { version = "0.11.0", optional = true }
 tonic-reflection = { version = "0.11.0", optional = true }
 tokio-stream = { version = "0.1.14", optional = true }
 
+# Malloc trim hack for linux
+[target.'cfg(target_os = "linux")'.dependencies]
+libc = "0.2.149"
+# else use mimalloc
+[target.'cfg(not(target_os = "linux"))'.dependencies]
+mimalloc = { version = "*", features = ["no_thp"] }
+
 [dev-dependencies]
 insta = { git = "https://github.com/OlivierDehaene/insta", rev = "f4f98c0410b91fb5a28b10df98e4422955be9c2c", features = ["yaml"] }
 is_close = "0.1.3"
-reqwest = { version = "0.11.22", features = ["json"] }
+reqwest = { version = "0.12.5", features = ["json"] }
 
 [build-dependencies]
 vergen = { version = "8.0.0", features = ["build", "git", "gitcl"] }
-tonic-build = { version = "0.10.2", optional = true }
+tonic-build = { version = "0.11.0", optional = true }
 
 [features]
 default = ["candle", "http"]
-http = ["dep:axum", "dep:axum-tracing-opentelemetry", "dep:tower-http", "dep:utoipa", "dep:utoipa-swagger-ui"]
+http = ["dep:axum", "dep:axum-tracing-opentelemetry", "dep:base64", "dep:tower-http", "dep:utoipa", "dep:utoipa-swagger-ui"]
 grpc = ["metrics-exporter-prometheus/http-listener", "dep:prost", "dep:tonic", "dep:tonic-health", "dep:tonic-reflection", "dep:tonic-build", "dep:async-stream", "dep:tokio-stream"]
 metal = ["text-embeddings-backend/metal"]
 mkl = ["text-embeddings-backend/mkl"]
diff --git a/router/src/grpc/server.rs b/router/src/grpc/server.rs
index 913455b3..d3666214 100644
--- a/router/src/grpc/server.rs
+++ b/router/src/grpc/server.rs
@@ -1,7 +1,7 @@
 use crate::grpc::pb::tei::v1::{
     EmbedAllRequest, EmbedAllResponse, EmbedSparseRequest, EmbedSparseResponse, EncodeRequest,
     EncodeResponse, PredictPairRequest, RerankStreamRequest, SimpleToken, SparseValue,
-    TokenEmbedding,
+    TokenEmbedding, TruncationDirection,
 };
 use crate::grpc::{
     DecodeRequest, DecodeResponse, EmbedRequest, EmbedResponse, InfoRequest, InfoResponse,
@@ -80,9 +80,17 @@ impl TextEmbeddingsService {
         let start_time = Instant::now();
 
         let compute_chars = request.inputs.chars().count();
+        let truncation_direction = convert_truncation_direction(request.truncation_direction);
         let response = self
             .infer
-            .embed_pooled(request.inputs, request.truncate, request.normalize, permit)
+            .embed_pooled(
+                request.inputs,
+                request.truncate,
+                truncation_direction,
+                request.prompt_name,
+                request.normalize,
+                permit,
+            )
             .await
             .map_err(ErrorResponse::from)?;
 
@@ -128,9 +136,16 @@ impl TextEmbeddingsService {
         let start_time = Instant::now();
 
         let compute_chars = request.inputs.chars().count();
+        let truncation_direction = convert_truncation_direction(request.truncation_direction);
         let response = self
             .infer
-            .embed_sparse(request.inputs, request.truncate, permit)
+            .embed_sparse(
+                request.inputs,
+                request.truncate,
+                truncation_direction,
+                request.prompt_name,
+                permit,
+            )
             .await
             .map_err(ErrorResponse::from)?;
 
@@ -187,9 +202,16 @@ impl TextEmbeddingsService {
         let start_time = Instant::now();
 
         let compute_chars = request.inputs.chars().count();
+        let truncation_direction = convert_truncation_direction(request.truncation_direction);
         let response = self
             .infer
-            .embed_all(request.inputs, request.truncate, permit)
+            .embed_all(
+                request.inputs,
+                request.truncate,
+                truncation_direction,
+                request.prompt_name,
+                permit,
+            )
             .await
             .map_err(ErrorResponse::from)?;
 
@@ -236,6 +258,7 @@ impl TextEmbeddingsService {
         &self,
         inputs: I,
         truncate: bool,
+        truncation_direction: tokenizers::TruncationDirection,
         raw_scores: bool,
         permit: OwnedSemaphorePermit,
     ) -> Result<(PredictResponse, ResponseMetadata), Status> {
@@ -251,7 +274,7 @@ impl TextEmbeddingsService {
 
         let response = self
             .infer
-            .predict(inputs, truncate, raw_scores, permit)
+            .predict(inputs, truncate, truncation_direction, raw_scores, permit)
             .await
             .map_err(ErrorResponse::from)?;
 
@@ -306,11 +329,17 @@ impl TextEmbeddingsService {
     #[instrument(skip_all)]
     async fn tokenize_inner(&self, request: EncodeRequest) -> Result<EncodeResponse, Status> {
         let inputs = request.inputs;
-        let encoding = self
+        let (encoded_inputs, encoding) = self
             .infer
-            .tokenize(inputs.clone(), request.add_special_tokens)
+            .tokenize(
+                inputs.clone(),
+                request.add_special_tokens,
+                request.prompt_name,
+            )
             .await
             .map_err(ErrorResponse::from)?;
+        let inputs = encoded_inputs.unwrap_or(inputs);
+
         let tokens: Vec<SimpleToken> = encoding
             .get_ids()
             .iter()
@@ -569,7 +598,8 @@ impl grpc::embed_server::Embed for TextEmbeddingsService {
         &self,
         request: Request<EmbedRequest>,
     ) -> Result<Response<EmbedResponse>, Status> {
-        metrics::increment_counter!("te_request_count", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         let permit = self
             .infer
@@ -580,7 +610,8 @@ impl grpc::embed_server::Embed for TextEmbeddingsService {
         let (response, metadata) = self.embed_pooled_inner(request, permit).await?;
         let headers = HeaderMap::from(metadata);
 
-        metrics::increment_counter!("te_request_success", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         Ok(Response::from_parts(
             MetadataMap::from_headers(headers),
@@ -609,7 +640,8 @@ impl grpc::embed_server::Embed for TextEmbeddingsService {
         &self,
         request: Request<EmbedSparseRequest>,
     ) -> Result<Response<EmbedSparseResponse>, Status> {
-        metrics::increment_counter!("te_request_count", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         let permit = self
             .infer
@@ -620,7 +652,8 @@ impl grpc::embed_server::Embed for TextEmbeddingsService {
         let (response, metadata) = self.embed_sparse_inner(request, permit).await?;
         let headers = HeaderMap::from(metadata);
 
-        metrics::increment_counter!("te_request_success", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         Ok(Response::from_parts(
             MetadataMap::from_headers(headers),
@@ -649,7 +682,8 @@ impl grpc::embed_server::Embed for TextEmbeddingsService {
         &self,
         request: Request<EmbedAllRequest>,
     ) -> Result<Response<EmbedAllResponse>, Status> {
-        metrics::increment_counter!("te_request_count", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         let permit = self
             .infer
@@ -660,7 +694,8 @@ impl grpc::embed_server::Embed for TextEmbeddingsService {
         let (response, metadata) = self.embed_all_inner(request, permit).await?;
         let headers = HeaderMap::from(metadata);
 
-        metrics::increment_counter!("te_request_success", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         Ok(Response::from_parts(
             MetadataMap::from_headers(headers),
@@ -693,7 +728,8 @@ impl grpc::predict_server::Predict for TextEmbeddingsService {
         &self,
         request: Request<PredictRequest>,
     ) -> Result<Response<PredictResponse>, Status> {
-        metrics::increment_counter!("te_request_count", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         let permit = self
             .infer
@@ -701,12 +737,20 @@ impl grpc::predict_server::Predict for TextEmbeddingsService {
             .map_err(ErrorResponse::from)?;
 
         let request = request.into_inner();
+        let truncation_direction = convert_truncation_direction(request.truncation_direction);
         let (response, metadata) = self
-            .predict_inner(request.inputs, request.truncate, request.raw_scores, permit)
+            .predict_inner(
+                request.inputs,
+                request.truncate,
+                truncation_direction,
+                request.raw_scores,
+                permit,
+            )
             .await?;
         let headers = HeaderMap::from(metadata);
 
-        metrics::increment_counter!("te_request_success", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         Ok(Response::from_parts(
             MetadataMap::from_headers(headers),
@@ -719,7 +763,8 @@ impl grpc::predict_server::Predict for TextEmbeddingsService {
         &self,
         request: Request<PredictPairRequest>,
     ) -> Result<Response<PredictResponse>, Status> {
-        metrics::increment_counter!("te_request_count", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
         let request = request.into_inner();
 
         let mut inputs = request.inputs;
@@ -743,12 +788,20 @@ impl grpc::predict_server::Predict for TextEmbeddingsService {
             .try_acquire_permit()
             .map_err(ErrorResponse::from)?;
 
+        let truncation_direction = convert_truncation_direction(request.truncation_direction);
         let (response, metadata) = self
-            .predict_inner(inputs, request.truncate, request.raw_scores, permit)
+            .predict_inner(
+                inputs,
+                request.truncate,
+                truncation_direction,
+                request.raw_scores,
+                permit,
+            )
             .await?;
         let headers = HeaderMap::from(metadata);
 
-        metrics::increment_counter!("te_request_success", "method" => "single");
+        let counter = metrics::counter!("te_request_count", "method" => "single");
+        counter.increment(1);
 
         Ok(Response::from_parts(
             MetadataMap::from_headers(headers),
@@ -767,8 +820,15 @@ impl grpc::predict_server::Predict for TextEmbeddingsService {
         // Clone for move below
         let clone = self.clone();
         let function = |req: PredictRequest, permit: OwnedSemaphorePermit| async move {
+            let truncation_direction = convert_truncation_direction(req.truncation_direction);
             clone
-                .predict_inner(req.inputs, req.truncate, req.raw_scores, permit)
+                .predict_inner(
+                    req.inputs,
+                    req.truncate,
+                    truncation_direction,
+                    req.raw_scores,
+                    permit,
+                )
                 .await
         };
 
@@ -800,8 +860,15 @@ impl grpc::predict_server::Predict for TextEmbeddingsService {
                 }
             };
 
+            let truncation_direction = convert_truncation_direction(req.truncation_direction);
             clone
-                .predict_inner(inputs, req.truncate, req.raw_scores, permit)
+                .predict_inner(
+                    inputs,
+                    req.truncate,
+                    truncation_direction,
+                    req.raw_scores,
+                    permit,
+                )
                 .await
         };
 
@@ -838,20 +905,23 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
                 error: message,
                 error_type: ErrorType::Validation,
             };
-            metrics::increment_counter!("te_request_failure", "err" => "validation");
+            let counter = metrics::counter!("te_request_failure", "err" => "validation");
+            counter.increment(1);
             Err(err)?;
         }
 
         match &self.info.model_type {
             ModelType::Classifier(_) => {
-                metrics::increment_counter!("te_request_failure", "err" => "model_type");
+                let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+                counter.increment(1);
                 let message = "model is not a re-ranker model".to_string();
                 tracing::error!("{message}");
                 Err(Status::new(Code::FailedPrecondition, message))
             }
             ModelType::Reranker(_) => Ok(()),
             ModelType::Embedding(_) => {
-                metrics::increment_counter!("te_request_failure", "err" => "model_type");
+                let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+                counter.increment(1);
                 let message = "model is not a classifier model".to_string();
                 tracing::error!("{message}");
                 Err(Status::new(Code::FailedPrecondition, message))
@@ -862,12 +932,19 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
         let rerank_inner = move |query: String,
                                  text: String,
                                  truncate: bool,
+                                 truncation_direction: tokenizers::TruncationDirection,
                                  raw_scores: bool,
                                  infer: Infer| async move {
             let permit = infer.acquire_permit().await;
 
             let response = infer
-                .predict((query, text), truncate, raw_scores, permit)
+                .predict(
+                    (query, text),
+                    truncate,
+                    truncation_direction,
+                    raw_scores,
+                    permit,
+                )
                 .await
                 .map_err(ErrorResponse::from)?;
 
@@ -882,7 +959,8 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
             ))
         };
 
-        metrics::increment_counter!("te_request_count", "method" => "batch");
+        let counter = metrics::counter!("te_request_count", "method" => "batch");
+        counter.increment(1);
 
         let batch_size = request.texts.len();
         if batch_size > self.info.max_client_batch_size {
@@ -895,13 +973,15 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
                 error: message,
                 error_type: ErrorType::Validation,
             };
-            metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+            let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+            counter.increment(1);
             Err(err)?;
         }
 
         let mut futures = Vec::with_capacity(batch_size);
         let query_chars = request.query.chars().count();
         let mut total_compute_chars = query_chars * batch_size;
+        let truncation_direction = convert_truncation_direction(request.truncation_direction);
 
         for text in &request.texts {
             total_compute_chars += text.chars().count();
@@ -910,6 +990,7 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
                 request.query.clone(),
                 text.clone(),
                 request.truncate,
+                truncation_direction,
                 request.raw_scores,
                 local_infer,
             ))
@@ -958,7 +1039,8 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
 
         let batch_size = batch_size as u64;
 
-        metrics::increment_counter!("te_request_success", "method" => "batch");
+        let counter = metrics::counter!("te_request_success", "method" => "batch");
+        counter.increment(1);
 
         let response_metadata = ResponseMetadata::new(
             total_compute_chars,
@@ -1008,14 +1090,16 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
         // Check model type
         match &self.info.model_type {
             ModelType::Classifier(_) => {
-                metrics::increment_counter!("te_request_failure", "err" => "model_type");
+                let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+                counter.increment(1);
                 let message = "model is not a re-ranker model".to_string();
                 tracing::error!("{message}");
                 Err(Status::new(Code::FailedPrecondition, message))
             }
             ModelType::Reranker(_) => Ok(()),
             ModelType::Embedding(_) => {
-                metrics::increment_counter!("te_request_failure", "err" => "model_type");
+                let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+                counter.increment(1);
                 let message = "model is not a classifier model".to_string();
                 tracing::error!("{message}");
                 Err(Status::new(Code::FailedPrecondition, message))
@@ -1027,11 +1111,18 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
                                  query: String,
                                  text: String,
                                  truncate: bool,
+                                 truncation_direction: tokenizers::TruncationDirection,
                                  raw_scores: bool,
                                  infer: Infer,
                                  permit: OwnedSemaphorePermit| async move {
             let response = infer
-                .predict((query, text.clone()), truncate, raw_scores, permit)
+                .predict(
+                    (query, text.clone()),
+                    truncate,
+                    truncation_direction,
+                    raw_scores,
+                    permit,
+                )
                 .await
                 .map_err(ErrorResponse::from)?;
 
@@ -1048,14 +1139,22 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
             ))
         };
 
-        metrics::increment_counter!("te_request_count", "method" => "batch");
+        let counter = metrics::counter!("te_request_count", "method" => "batch");
+        counter.increment(1);
 
         let mut request_stream = request.into_inner();
 
         // Create bounded channel to have an upper bound of spawned tasks
         // We will have at most `max_parallel_stream_requests` messages from this stream in the queue
         let (rerank_sender, mut rerank_receiver) = mpsc::channel::<(
-            (usize, String, String, bool, bool),
+            (
+                usize,
+                String,
+                String,
+                bool,
+                tokenizers::TruncationDirection,
+                bool,
+            ),
             oneshot::Sender<
                 Result<(usize, usize, Duration, Duration, Duration, f32, String), ErrorResponse>,
             >,
@@ -1066,8 +1165,10 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
 
         // Background task that uses the bounded channel
         tokio::spawn(async move {
-            while let Some(((index, query, text, truncate, raw_scores), mut sender)) =
-                rerank_receiver.recv().await
+            while let Some((
+                (index, query, text, truncate, truncation_direction, raw_scores),
+                mut sender,
+            )) = rerank_receiver.recv().await
             {
                 // Wait on permit before spawning the task to avoid creating more tasks than needed
                 let permit = local_infer.acquire_permit().await;
@@ -1079,7 +1180,7 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
                 tokio::spawn(async move {
                     // Select on closed to cancel work if the stream was closed
                     tokio::select! {
-                    result = rerank_inner(index, query, text, truncate, raw_scores, task_infer, permit) => {
+                    result = rerank_inner(index, query, text, truncate, truncation_direction, raw_scores, task_infer, permit) => {
                         let _ = sender.send(result);
                     }
                     _ = sender.closed() => {}
@@ -1118,6 +1219,7 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
             total_compute_chars += request.query.chars().count();
             total_compute_chars += request.text.chars().count();
 
+            let truncation_direction = convert_truncation_direction(request.truncation_direction);
             rerank_sender
                 .send((
                     (
@@ -1125,6 +1227,7 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
                         request.query,
                         request.text,
                         request.truncate,
+                        truncation_direction,
                         raw_scores.unwrap(),
                     ),
                     result_sender,
@@ -1186,7 +1289,8 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
                 error: message,
                 error_type: ErrorType::Backend,
             };
-            metrics::increment_counter!("te_request_failure", "err" => "missing_values");
+            let counter = metrics::counter!("te_request_failure", "err" => "missing_values");
+            counter.increment(1);
             Err(err)?;
         }
 
@@ -1196,7 +1300,8 @@ impl grpc::rerank_server::Rerank for TextEmbeddingsService {
 
         let batch_size = batch_size as u64;
 
-        metrics::increment_counter!("te_request_success", "method" => "batch");
+        let counter = metrics::counter!("te_request_success", "method" => "batch");
+        counter.increment(1);
 
         let response_metadata = ResponseMetadata::new(
             total_compute_chars,
@@ -1434,3 +1539,10 @@ impl From<ErrorResponse> for Status {
         Status::new(code, value.error)
     }
 }
+
+fn convert_truncation_direction(value: i32) -> tokenizers::TruncationDirection {
+    match TruncationDirection::try_from(value).expect("Unexpected enum value") {
+        TruncationDirection::Right => tokenizers::TruncationDirection::Right,
+        TruncationDirection::Left => tokenizers::TruncationDirection::Left,
+    }
+}
diff --git a/router/src/http/server.rs b/router/src/http/server.rs
index 6c48ad3e..8f57fbb2 100644
--- a/router/src/http/server.rs
+++ b/router/src/http/server.rs
@@ -1,11 +1,12 @@
 /// HTTP Server logic
 use crate::http::types::{
     DecodeRequest, DecodeResponse, EmbedAllRequest, EmbedAllResponse, EmbedRequest, EmbedResponse,
-    EmbedSparseRequest, EmbedSparseResponse, Input, InputIds, InputType, OpenAICompatEmbedding,
-    OpenAICompatErrorResponse, OpenAICompatRequest, OpenAICompatResponse, OpenAICompatUsage,
-    PredictInput, PredictRequest, PredictResponse, Prediction, Rank, RerankRequest, RerankResponse,
-    Sequence, SimpleToken, SparseValue, TokenizeInput, TokenizeRequest, TokenizeResponse,
-    VertexPrediction, VertexRequest, VertexResponse,
+    EmbedSparseRequest, EmbedSparseResponse, Embedding, EncodingFormat, Input, InputIds, InputType,
+    OpenAICompatEmbedding, OpenAICompatErrorResponse, OpenAICompatRequest, OpenAICompatResponse,
+    OpenAICompatUsage, PredictInput, PredictRequest, PredictResponse, Prediction, Rank,
+    RerankRequest, RerankResponse, Sequence, SimpleToken, SparseValue, TokenizeInput,
+    TokenizeRequest, TokenizeResponse, TruncationDirection, VertexPrediction, VertexRequest,
+    VertexResponse,
 };
 use crate::{
     shutdown, ClassifierModel, EmbeddingModel, ErrorResponse, ErrorType, Info, ModelType,
@@ -19,6 +20,8 @@ use axum::http::{Method, StatusCode};
 use axum::routing::{get, post};
 use axum::{http, Json, Router};
 use axum_tracing_opentelemetry::middleware::OtelAxumLayer;
+use base64::prelude::BASE64_STANDARD;
+use base64::Engine;
 use futures::future::join_all;
 use futures::FutureExt;
 use http::header::AUTHORIZATION;
@@ -103,7 +106,6 @@ async fn predict(
     // Closure for predict
     let predict_inner = move |inputs: Sequence,
                               truncate: bool,
-                              raw_scores: bool,
                               infer: Infer,
                               info: Info,
                               permit: Option<OwnedSemaphorePermit>| async move {
@@ -113,7 +115,13 @@ async fn predict(
         };
 
         let response = infer
-            .predict(inputs, truncate, raw_scores, permit)
+            .predict(
+                inputs,
+                truncate,
+                req.truncation_direction.into(),
+                req.raw_scores,
+                permit,
+            )
             .await
             .map_err(ErrorResponse::from)?;
 
@@ -155,21 +163,16 @@ async fn predict(
 
     let (response, metadata) = match req.inputs {
         PredictInput::Single(inputs) => {
-            metrics::increment_counter!("te_request_count", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             let compute_chars = inputs.count_chars();
             let permit = infer.try_acquire_permit().map_err(ErrorResponse::from)?;
-            let (prompt_tokens, tokenization, queue, inference, predictions) = predict_inner(
-                inputs,
-                truncate,
-                req.raw_scores,
-                infer.0,
-                info.0,
-                Some(permit),
-            )
-            .await?;
+            let (prompt_tokens, tokenization, queue, inference, predictions) =
+                predict_inner(inputs, truncate, infer.0, info.0, Some(permit)).await?;
 
-            metrics::increment_counter!("te_request_success", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             (
                 PredictResponse::Single(predictions),
@@ -184,7 +187,8 @@ async fn predict(
             )
         }
         PredictInput::Batch(inputs) => {
-            metrics::increment_counter!("te_request_count", "method" => "batch");
+            let counter = metrics::counter!("te_request_count", "method" => "batch");
+            counter.increment(1);
 
             let batch_size = inputs.len();
             if batch_size > info.max_client_batch_size {
@@ -197,7 +201,8 @@ async fn predict(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+                let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -211,7 +216,6 @@ async fn predict(
                 futures.push(predict_inner(
                     input,
                     truncate,
-                    req.raw_scores,
                     local_infer.0,
                     local_info.0,
                     None,
@@ -237,7 +241,8 @@ async fn predict(
             }
             let batch_size = batch_size as u64;
 
-            metrics::increment_counter!("te_request_success", "method" => "batch");
+            let counter = metrics::counter!("te_request_success", "method" => "batch");
+            counter.increment(1);
 
             (
                 PredictResponse::Batch(predictions),
@@ -301,14 +306,16 @@ async fn rerank(
             error: message,
             error_type: ErrorType::Validation,
         };
-        metrics::increment_counter!("te_request_failure", "err" => "validation");
+        let counter = metrics::counter!("te_request_failure", "err" => "validation");
+        counter.increment(1);
         Err(err)?;
     }
 
     match &info.model_type {
         ModelType::Reranker(_) => Ok(()),
         ModelType::Classifier(_) | ModelType::Embedding(_) => {
-            metrics::increment_counter!("te_request_failure", "err" => "model_type");
+            let counter = metrics::counter!("te_request_failure", "err" => "model_type");
+            counter.increment(1);
             let message = "model is not a re-ranker model".to_string();
             Err(TextEmbeddingsError::Backend(BackendError::Inference(
                 message,
@@ -321,15 +328,17 @@ async fn rerank(
     })?;
 
     // Closure for rerank
-    let rerank_inner = move |query: String,
-                             text: String,
-                             truncate: bool,
-                             raw_scores: bool,
-                             infer: Infer| async move {
+    let rerank_inner = move |query: String, text: String, truncate: bool, infer: Infer| async move {
         let permit = infer.acquire_permit().await;
 
         let response = infer
-            .predict((query, text), truncate, raw_scores, permit)
+            .predict(
+                (query, text),
+                truncate,
+                req.truncation_direction.into(),
+                req.raw_scores,
+                permit,
+            )
             .await
             .map_err(ErrorResponse::from)?;
 
@@ -347,7 +356,8 @@ async fn rerank(
     let truncate = req.truncate.unwrap_or(info.auto_truncate);
 
     let (response, metadata) = {
-        metrics::increment_counter!("te_request_count", "method" => "batch");
+        let counter = metrics::counter!("te_request_count", "method" => "batch");
+        counter.increment(1);
 
         let batch_size = req.texts.len();
         if batch_size > info.max_client_batch_size {
@@ -360,7 +370,8 @@ async fn rerank(
                 error: message,
                 error_type: ErrorType::Validation,
             };
-            metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+            let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+            counter.increment(1);
             Err(err)?;
         }
 
@@ -375,7 +386,6 @@ async fn rerank(
                 req.query.clone(),
                 text.clone(),
                 truncate,
-                req.raw_scores,
                 local_infer.0,
             ))
         }
@@ -419,7 +429,8 @@ async fn rerank(
 
         let batch_size = batch_size as u64;
 
-        metrics::increment_counter!("te_request_success", "method" => "batch");
+        let counter = metrics::counter!("te_request_success", "method" => "batch");
+        counter.increment(1);
 
         (
             RerankResponse(ranks),
@@ -478,17 +489,26 @@ async fn embed(
 
     let (response, metadata) = match req.inputs {
         Input::Single(input) => {
-            metrics::increment_counter!("te_request_count", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             let compute_chars = input.count_chars();
 
             let permit = infer.try_acquire_permit().map_err(ErrorResponse::from)?;
             let response = infer
-                .embed_pooled(input, truncate, req.normalize, permit)
+                .embed_pooled(
+                    input,
+                    truncate,
+                    req.truncation_direction.into(),
+                    req.prompt_name,
+                    req.normalize,
+                    permit,
+                )
                 .await
                 .map_err(ErrorResponse::from)?;
 
-            metrics::increment_counter!("te_request_success", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             (
                 EmbedResponse(vec![response.results]),
@@ -503,7 +523,8 @@ async fn embed(
             )
         }
         Input::Batch(inputs) => {
-            metrics::increment_counter!("te_request_count", "method" => "batch");
+            let counter = metrics::counter!("te_request_count", "method" => "batch");
+            counter.increment(1);
 
             if inputs.is_empty() {
                 let message = "`inputs` cannot be empty".to_string();
@@ -512,7 +533,8 @@ async fn embed(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "validation");
+                let counter = metrics::counter!("te_request_failure", "err" => "validation");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -527,7 +549,8 @@ async fn embed(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+                let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -538,10 +561,18 @@ async fn embed(
                 compute_chars += input.count_chars();
 
                 let local_infer = infer.clone();
+                let prompt_name = req.prompt_name.clone();
                 futures.push(async move {
                     let permit = local_infer.acquire_permit().await;
                     local_infer
-                        .embed_pooled(input, truncate, req.normalize, permit)
+                        .embed_pooled(
+                            input,
+                            truncate,
+                            req.truncation_direction.into(),
+                            prompt_name,
+                            req.normalize,
+                            permit,
+                        )
                         .await
                 })
             }
@@ -566,7 +597,8 @@ async fn embed(
             }
             let batch_size = batch_size as u64;
 
-            metrics::increment_counter!("te_request_success", "method" => "batch");
+            let counter = metrics::counter!("te_request_success", "method" => "batch");
+            counter.increment(1);
 
             (
                 EmbedResponse(embeddings),
@@ -635,17 +667,25 @@ async fn embed_sparse(
 
     let (response, metadata) = match req.inputs {
         Input::Single(input) => {
-            metrics::increment_counter!("te_request_count", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             let compute_chars = input.count_chars();
 
             let permit = infer.try_acquire_permit().map_err(ErrorResponse::from)?;
             let response = infer
-                .embed_sparse(input, truncate, permit)
+                .embed_sparse(
+                    input,
+                    truncate,
+                    req.truncation_direction.into(),
+                    req.prompt_name,
+                    permit,
+                )
                 .await
                 .map_err(ErrorResponse::from)?;
 
-            metrics::increment_counter!("te_request_success", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             (
                 EmbedSparseResponse(vec![sparsify(response.results)]),
@@ -660,7 +700,8 @@ async fn embed_sparse(
             )
         }
         Input::Batch(inputs) => {
-            metrics::increment_counter!("te_request_count", "method" => "batch");
+            let counter = metrics::counter!("te_request_count", "method" => "batch");
+            counter.increment(1);
 
             if inputs.is_empty() {
                 let message = "`inputs` cannot be empty".to_string();
@@ -669,7 +710,8 @@ async fn embed_sparse(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "validation");
+                let counter = metrics::counter!("te_request_failure", "err" => "validation");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -684,7 +726,8 @@ async fn embed_sparse(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+                let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -695,9 +738,19 @@ async fn embed_sparse(
                 compute_chars += input.count_chars();
 
                 let local_infer = infer.clone();
+                let prompt_name = req.prompt_name.clone();
                 futures.push(async move {
                     let permit = local_infer.acquire_permit().await;
-                    let response = local_infer.embed_sparse(input, truncate, permit).await?;
+                    let response = local_infer
+                        .embed_sparse(
+                            input,
+                            truncate,
+                            req.truncation_direction.into(),
+                            prompt_name,
+                            permit,
+                        )
+                        .await?;
+
                     Ok((sparsify(response.results), response.metadata))
                 })
             }
@@ -722,7 +775,8 @@ async fn embed_sparse(
             }
             let batch_size = batch_size as u64;
 
-            metrics::increment_counter!("te_request_success", "method" => "batch");
+            let counter = metrics::counter!("te_request_success", "method" => "batch");
+            counter.increment(1);
 
             (
                 EmbedSparseResponse(embeddings),
@@ -783,17 +837,25 @@ async fn embed_all(
 
     let (response, metadata) = match req.inputs {
         Input::Single(input) => {
-            metrics::increment_counter!("te_request_count", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             let compute_chars = input.count_chars();
 
             let permit = infer.try_acquire_permit().map_err(ErrorResponse::from)?;
             let response = infer
-                .embed_all(input, truncate, permit)
+                .embed_all(
+                    input,
+                    truncate,
+                    req.truncation_direction.into(),
+                    req.prompt_name,
+                    permit,
+                )
                 .await
                 .map_err(ErrorResponse::from)?;
 
-            metrics::increment_counter!("te_request_success", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             (
                 EmbedAllResponse(vec![response.results]),
@@ -808,7 +870,8 @@ async fn embed_all(
             )
         }
         Input::Batch(inputs) => {
-            metrics::increment_counter!("te_request_count", "method" => "batch");
+            let counter = metrics::counter!("te_request_count", "method" => "batch");
+            counter.increment(1);
 
             if inputs.is_empty() {
                 let message = "`inputs` cannot be empty".to_string();
@@ -817,7 +880,8 @@ async fn embed_all(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "validation");
+                let counter = metrics::counter!("te_request_failure", "err" => "validation");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -832,7 +896,8 @@ async fn embed_all(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+                let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -843,9 +908,18 @@ async fn embed_all(
                 compute_chars += input.count_chars();
 
                 let local_infer = infer.clone();
+                let prompt_name = req.prompt_name.clone();
                 futures.push(async move {
                     let permit = local_infer.acquire_permit().await;
-                    local_infer.embed_all(input, truncate, permit).await
+                    local_infer
+                        .embed_all(
+                            input,
+                            truncate,
+                            req.truncation_direction.into(),
+                            prompt_name,
+                            permit,
+                        )
+                        .await
                 })
             }
             let results = join_all(futures)
@@ -869,7 +943,8 @@ async fn embed_all(
             }
             let batch_size = batch_size as u64;
 
-            metrics::increment_counter!("te_request_success", "method" => "batch");
+            let counter = metrics::counter!("te_request_success", "method" => "batch");
+            counter.increment(1);
 
             (
                 EmbedAllResponse(embeddings),
@@ -923,6 +998,21 @@ async fn openai_embed(
     Json(req): Json<OpenAICompatRequest>,
 ) -> Result<(HeaderMap, Json<OpenAICompatResponse>), (StatusCode, Json<OpenAICompatErrorResponse>)>
 {
+    let encode_embedding = |array: Vec<f32>| {
+        match req.encoding_format {
+            EncodingFormat::Float => Embedding::Float(array),
+            EncodingFormat::Base64 => {
+                // Unsafe is fine here since we do not violate memory ownership: bytes
+                // is only used in this scope and we return an owned string
+                let bytes = unsafe {
+                    std::slice::from_raw_parts(array.as_ptr() as *const u8, array.len() * 4)
+                };
+
+                Embedding::Base64(BASE64_STANDARD.encode(bytes))
+            }
+        }
+    };
+
     let span = tracing::Span::current();
     let start_time = Instant::now();
 
@@ -930,22 +1020,32 @@ async fn openai_embed(
 
     let (embeddings, metadata) = match req.input {
         Input::Single(input) => {
-            metrics::increment_counter!("te_request_count", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
             let compute_chars = input.count_chars();
 
             let permit = infer.try_acquire_permit().map_err(ErrorResponse::from)?;
             let response = infer
-                .embed_pooled(input, truncate, true, permit)
+                .embed_pooled(
+                    input,
+                    truncate,
+                    tokenizers::TruncationDirection::Right,
+                    None,
+                    true,
+                    permit,
+                )
                 .await
                 .map_err(ErrorResponse::from)?;
 
-            metrics::increment_counter!("te_request_success", "method" => "single");
+            let counter = metrics::counter!("te_request_count", "method" => "single");
+            counter.increment(1);
 
+            let embedding = encode_embedding(response.results);
             (
                 vec![OpenAICompatEmbedding {
                     object: "embedding",
-                    embedding: response.results,
+                    embedding,
                     index: 0,
                 }],
                 ResponseMetadata::new(
@@ -959,7 +1059,8 @@ async fn openai_embed(
             )
         }
         Input::Batch(inputs) => {
-            metrics::increment_counter!("te_request_count", "method" => "batch");
+            let counter = metrics::counter!("te_request_count", "method" => "batch");
+            counter.increment(1);
 
             if inputs.is_empty() {
                 let message = "`inputs` cannot be empty".to_string();
@@ -968,7 +1069,8 @@ async fn openai_embed(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "validation");
+                let counter = metrics::counter!("te_request_failure", "err" => "validation");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -983,7 +1085,8 @@ async fn openai_embed(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+                let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -997,7 +1100,14 @@ async fn openai_embed(
                 futures.push(async move {
                     let permit = local_infer.acquire_permit().await;
                     local_infer
-                        .embed_pooled(input, truncate, true, permit)
+                        .embed_pooled(
+                            input,
+                            truncate,
+                            tokenizers::TruncationDirection::Right,
+                            None,
+                            true,
+                            permit,
+                        )
                         .await
                 })
             }
@@ -1018,15 +1128,17 @@ async fn openai_embed(
                 total_queue_time += r.metadata.queue.as_nanos() as u64;
                 total_inference_time += r.metadata.inference.as_nanos() as u64;
                 total_compute_tokens += r.metadata.prompt_tokens;
+                let embedding = encode_embedding(r.results);
                 embeddings.push(OpenAICompatEmbedding {
                     object: "embedding",
-                    embedding: r.results,
+                    embedding,
                     index: i,
                 });
             }
             let batch_size = batch_size as u64;
 
-            metrics::increment_counter!("te_request_success", "method" => "batch");
+            let counter = metrics::counter!("te_request_success", "method" => "batch");
+            counter.increment(1);
 
             (
                 embeddings,
@@ -1080,11 +1192,16 @@ async fn tokenize(
     info: Extension<Info>,
     Json(req): Json<TokenizeRequest>,
 ) -> Result<Json<TokenizeResponse>, (StatusCode, Json<ErrorResponse>)> {
-    let tokenize_inner = move |input: String, add_special_tokens: bool, infer: Infer| async move {
-        let encoding = infer
-            .tokenize(input.clone(), add_special_tokens)
+    let tokenize_inner = move |input: String,
+                               add_special_tokens: bool,
+                               prompt_name: Option<String>,
+                               infer: Infer| async move {
+        let (encoded_input, encoding) = infer
+            .tokenize(input.clone(), add_special_tokens, prompt_name)
             .await
             .map_err(ErrorResponse::from)?;
+        let input = encoded_input.unwrap_or(input);
+
         let tokens: Vec<SimpleToken> = encoding
             .get_ids()
             .iter()
@@ -1119,7 +1236,7 @@ async fn tokenize(
 
     let tokens = match req.inputs {
         TokenizeInput::Single(input) => {
-            vec![tokenize_inner(input, req.add_special_tokens, infer.0).await?]
+            vec![tokenize_inner(input, req.add_special_tokens, req.prompt_name, infer.0).await?]
         }
         TokenizeInput::Batch(inputs) => {
             if inputs.is_empty() {
@@ -1129,7 +1246,8 @@ async fn tokenize(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "validation");
+                let counter = metrics::counter!("te_request_failure", "err" => "validation");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -1144,7 +1262,8 @@ async fn tokenize(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+                let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -1153,6 +1272,7 @@ async fn tokenize(
                 futures.push(tokenize_inner(
                     input,
                     req.add_special_tokens,
+                    req.prompt_name.clone(),
                     infer.0.clone(),
                 ));
             }
@@ -1202,7 +1322,8 @@ async fn decode(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "validation");
+                let counter = metrics::counter!("te_request_failure", "err" => "validation");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -1217,7 +1338,8 @@ async fn decode(
                     error: message,
                     error_type: ErrorType::Validation,
                 };
-                metrics::increment_counter!("te_request_failure", "err" => "batch_size");
+                let counter = metrics::counter!("te_request_failure", "err" => "batch_size");
+                counter.increment(1);
                 Err(err)?;
             }
 
@@ -1362,6 +1484,8 @@ pub async fn run(
     Info,
     ModelType,
     ClassifierModel,
+    Embedding,
+    EncodingFormat,
     EmbeddingModel,
     PredictRequest,
     Prediction,
@@ -1385,6 +1509,7 @@ pub async fn run(
     TokenizeInput,
     TokenizeRequest,
     TokenizeResponse,
+    TruncationDirection,
     SimpleToken,
     InputType,
     InputIds,
@@ -1410,11 +1535,15 @@ pub async fn run(
     // map to go inside the option and then map to parse from String to HeaderValue
     // Finally, convert to AllowOrigin
     let allow_origin: Option<AllowOrigin> = cors_allow_origin.map(|cors_allow_origin| {
-        AllowOrigin::list(
-            cors_allow_origin
-                .into_iter()
-                .map(|origin| origin.parse::<HeaderValue>().unwrap()),
-        )
+        if cors_allow_origin.iter().any(|origin| origin == "*") {
+            AllowOrigin::any()
+        } else {
+            AllowOrigin::list(
+                cors_allow_origin
+                    .into_iter()
+                    .map(|origin| origin.parse::<HeaderValue>().unwrap()),
+            )
+        }
     });
 
     let prom_handle = prom_builder
@@ -1451,7 +1580,6 @@ pub async fn run(
 
     // Create router
     let mut app = Router::new()
-        .layer(DefaultBodyLimit::max(payload_limit))
         .merge(SwaggerUi::new("/docs").url("/api-doc/openapi.json", doc))
         // Base routes
         .route("/info", get(get_model_info))
@@ -1474,7 +1602,9 @@ pub async fn run(
         // AWS Sagemaker health route
         .route("/ping", get(health))
         // Prometheus metrics route
-        .route("/metrics", get(metrics));
+        .route("/metrics", get(metrics))
+        // Update payload limit
+        .layer(DefaultBodyLimit::max(payload_limit));
 
     #[cfg(feature = "google")]
     {
@@ -1548,7 +1678,9 @@ pub async fn run(
     }
 
     // Run server
-    let listener = tokio::net::TcpListener::bind(&addr).await.unwrap();
+    let listener = tokio::net::TcpListener::bind(&addr)
+        .await
+        .context(format!("Could not bind TCP Listener on {addr}"))?;
 
     tracing::info!("Starting HTTP server: {}", &addr);
     tracing::info!("Ready");
diff --git a/router/src/http/types.rs b/router/src/http/types.rs
index be514d40..4414ecb4 100644
--- a/router/src/http/types.rs
+++ b/router/src/http/types.rs
@@ -193,12 +193,31 @@ impl<'__s> ToSchema<'__s> for PredictInput {
     }
 }
 
+#[derive(Debug, Clone, Copy, PartialEq, Deserialize, ToSchema, Eq, Default)]
+pub(crate) enum TruncationDirection {
+    Left,
+    #[default]
+    Right,
+}
+
+impl From<TruncationDirection> for tokenizers::TruncationDirection {
+    fn from(value: TruncationDirection) -> Self {
+        match value {
+            TruncationDirection::Left => Self::Left,
+            TruncationDirection::Right => Self::Right,
+        }
+    }
+}
+
 #[derive(Deserialize, ToSchema)]
 pub(crate) struct PredictRequest {
     pub inputs: PredictInput,
     #[schema(default = "false", example = "false", nullable = true)]
     pub truncate: Option<bool>,
     #[serde(default)]
+    #[schema(default = "right", example = "right")]
+    pub truncation_direction: TruncationDirection,
+    #[serde(default)]
     #[schema(default = "false", example = "false")]
     pub raw_scores: bool,
 }
@@ -228,6 +247,9 @@ pub(crate) struct RerankRequest {
     #[schema(default = "false", example = "false", nullable = true)]
     pub truncate: Option<bool>,
     #[serde(default)]
+    #[schema(default = "right", example = "right")]
+    pub truncation_direction: TruncationDirection,
+    #[serde(default)]
     #[schema(default = "false", example = "false")]
     pub raw_scores: bool,
     #[serde(default)]
@@ -255,6 +277,7 @@ pub(crate) enum InputType {
     String(String),
     Ids(Vec<u32>),
 }
+
 impl InputType {
     pub(crate) fn count_chars(&self) -> usize {
         match self {
@@ -263,6 +286,7 @@ impl InputType {
         }
     }
 }
+
 impl From<InputType> for EncodingInput {
     fn from(value: InputType) -> Self {
         match value {
@@ -271,6 +295,7 @@ impl From<InputType> for EncodingInput {
         }
     }
 }
+
 #[derive(Deserialize, ToSchema)]
 #[serde(untagged)]
 pub(crate) enum Input {
@@ -278,6 +303,14 @@ pub(crate) enum Input {
     Batch(Vec<InputType>),
 }
 
+#[derive(Deserialize, ToSchema, Default)]
+#[serde(rename_all = "snake_case")]
+pub(crate) enum EncodingFormat {
+    #[default]
+    Float,
+    Base64,
+}
+
 #[derive(Deserialize, ToSchema)]
 pub(crate) struct OpenAICompatRequest {
     pub input: Input,
@@ -287,6 +320,16 @@ pub(crate) struct OpenAICompatRequest {
     #[allow(dead_code)]
     #[schema(nullable = true, example = "null")]
     pub user: Option<String>,
+    #[schema(default = "float", example = "float")]
+    #[serde(default)]
+    pub encoding_format: EncodingFormat,
+}
+
+#[derive(Serialize, ToSchema)]
+#[serde(untagged)]
+pub(crate) enum Embedding {
+    Float(Vec<f32>),
+    Base64(String),
 }
 
 #[derive(Serialize, ToSchema)]
@@ -294,7 +337,7 @@ pub(crate) struct OpenAICompatEmbedding {
     #[schema(example = "embedding")]
     pub object: &'static str,
     #[schema(example = json!([0.0, 1.0, 2.0]))]
-    pub embedding: Vec<f32>,
+    pub embedding: Embedding,
     #[schema(example = "0")]
     pub index: usize,
 }
@@ -323,6 +366,20 @@ pub(crate) struct EmbedRequest {
     #[serde(default)]
     #[schema(default = "false", example = "false", nullable = true)]
     pub truncate: Option<bool>,
+    #[serde(default)]
+    #[schema(default = "right", example = "right")]
+    pub truncation_direction: TruncationDirection,
+    /// The name of the prompt that should be used by for encoding. If not set, no prompt
+    /// will be applied.
+    ///
+    /// Must be a key in the `Sentence Transformers` configuration `prompts` dictionary.
+    ///
+    /// For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...},
+    /// then the sentence "What is the capital of France?" will be encoded as
+    /// "query: What is the capital of France?" because the prompt text will be prepended before
+    /// any text to encode.
+    #[schema(default = "null", example = "null", nullable = true)]
+    pub prompt_name: Option<String>,
     #[serde(default = "default_normalize")]
     #[schema(default = "true", example = "true")]
     pub normalize: bool,
@@ -342,6 +399,20 @@ pub(crate) struct EmbedSparseRequest {
     #[serde(default)]
     #[schema(default = "false", example = "false", nullable = true)]
     pub truncate: Option<bool>,
+    #[serde(default)]
+    #[schema(default = "right", example = "right")]
+    pub truncation_direction: TruncationDirection,
+    /// The name of the prompt that should be used by for encoding. If not set, no prompt
+    /// will be applied.
+    ///
+    /// Must be a key in the `Sentence Transformers` configuration `prompts` dictionary.
+    ///
+    /// For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...},
+    /// then the sentence "What is the capital of France?" will be encoded as
+    /// "query: What is the capital of France?" because the prompt text will be prepended before
+    /// any text to encode.
+    #[schema(default = "null", example = "null", nullable = true)]
+    pub prompt_name: Option<String>,
 }
 
 #[derive(Serialize, ToSchema)]
@@ -359,6 +430,20 @@ pub(crate) struct EmbedAllRequest {
     #[serde(default)]
     #[schema(default = "false", example = "false", nullable = true)]
     pub truncate: Option<bool>,
+    #[serde(default)]
+    #[schema(default = "right", example = "right")]
+    pub truncation_direction: TruncationDirection,
+    /// The name of the prompt that should be used by for encoding. If not set, no prompt
+    /// will be applied.
+    ///
+    /// Must be a key in the `Sentence Transformers` configuration `prompts` dictionary.
+    ///
+    /// For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...},
+    /// then the sentence "What is the capital of France?" will be encoded as
+    /// "query: What is the capital of France?" because the prompt text will be prepended before
+    /// any text to encode.
+    #[schema(default = "null", example = "null", nullable = true)]
+    pub prompt_name: Option<String>,
 }
 
 #[derive(Serialize, ToSchema)]
@@ -386,6 +471,17 @@ pub(crate) struct TokenizeRequest {
     #[serde(default = "default_add_special_tokens")]
     #[schema(default = "true", example = "true")]
     pub add_special_tokens: bool,
+    /// The name of the prompt that should be used by for encoding. If not set, no prompt
+    /// will be applied.
+    ///
+    /// Must be a key in the `Sentence Transformers` configuration `prompts` dictionary.
+    ///
+    /// For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...},
+    /// then the sentence "What is the capital of France?" will be encoded as
+    /// "query: What is the capital of France?" because the prompt text will be prepended before
+    /// any text to encode.
+    #[schema(default = "null", example = "null", nullable = true)]
+    pub prompt_name: Option<String>,
 }
 
 fn default_add_special_tokens() -> bool {
diff --git a/router/src/lib.rs b/router/src/lib.rs
index 177dde5a..411f5aeb 100644
--- a/router/src/lib.rs
+++ b/router/src/lib.rs
@@ -26,15 +26,18 @@ use std::fs;
 use std::net::{IpAddr, Ipv4Addr, SocketAddr};
 use std::path::Path;
 use std::time::{Duration, Instant};
-use text_embeddings_backend::DType;
+use text_embeddings_backend::{DType, Pool};
 use text_embeddings_core::download::{
-    download_artifacts, download_pool_config, download_st_config, ST_CONFIG_NAMES,
+    download_artifacts, download_new_st_config, download_pool_config, download_st_config,
+    ST_CONFIG_NAMES,
 };
 use text_embeddings_core::infer::Infer;
 use text_embeddings_core::queue::Queue;
 use text_embeddings_core::tokenization::Tokenization;
 use text_embeddings_core::TextEmbeddingsError;
-use tokenizers::Tokenizer;
+use tokenizers::processors::sequence::Sequence;
+use tokenizers::processors::template::TemplateProcessing;
+use tokenizers::{PostProcessorWrapper, Tokenizer};
 use tracing::Span;
 
 pub use logging::init_logging;
@@ -52,6 +55,8 @@ pub async fn run(
     max_batch_requests: Option<usize>,
     max_client_batch_size: usize,
     auto_truncate: bool,
+    default_prompt: Option<String>,
+    default_prompt_name: Option<String>,
     hf_api_token: Option<String>,
     hostname: Option<String>,
     port: u16,
@@ -60,6 +65,7 @@ pub async fn run(
     payload_limit: usize,
     api_key: Option<String>,
     otlp_endpoint: Option<String>,
+    otlp_service_name: String,
     cors_allow_origin: Option<Vec<String>>,
 ) -> Result<()> {
     let model_id_path = Path::new(&model_id);
@@ -90,6 +96,8 @@ pub async fn run(
 
         // Download sentence transformers config
         let _ = download_st_config(&api_repo).await;
+        // Download new sentence transformers config
+        let _ = download_new_st_config(&api_repo).await;
 
         // Download model from the Hub
         download_artifacts(&api_repo)
@@ -138,6 +146,28 @@ pub async fn run(
         "tokenizer.json not found. text-embeddings-inference only supports fast tokenizers",
     );
     tokenizer.with_padding(None);
+    // Qwen2 updates the post processor manually instead of into the tokenizer.json...
+    // https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct/blob/main/tokenization_qwen.py#L246
+    if config.model_type == "qwen2" {
+        let template = TemplateProcessing::builder()
+            .try_single("$A:0 <|endoftext|>:0")
+            .unwrap()
+            .try_pair("$A:0 <|endoftext|>:0 $B:1 <|endoftext|>:1")
+            .unwrap()
+            .special_tokens(vec![("<|endoftext|>", 151643)])
+            .build()
+            .unwrap();
+        match tokenizer.get_post_processor() {
+            None => tokenizer.with_post_processor(template),
+            Some(post_processor) => {
+                let post_processor = Sequence::new(vec![
+                    post_processor.clone(),
+                    PostProcessorWrapper::Template(template),
+                ]);
+                tokenizer.with_post_processor(post_processor)
+            }
+        };
+    }
 
     // Position IDs offset. Used for Roberta and camembert.
     let position_offset = if &config.model_type == "xlm-roberta"
@@ -170,12 +200,38 @@ pub async fn run(
 
     let tokenization_workers = tokenization_workers.unwrap_or_else(num_cpus::get_physical);
 
+    // Try to load new ST Config
+    let mut new_st_config: Option<NewSTConfig> = None;
+    let config_path = model_root.join("config_sentence_transformers.json");
+    if let Ok(config) = fs::read_to_string(config_path) {
+        new_st_config = Some(
+            serde_json::from_str(&config)
+                .context("Failed to parse `config_sentence_transformers.json`")?,
+        );
+    }
+    let prompts = new_st_config.and_then(|c| c.prompts);
+    let default_prompt = if let Some(default_prompt_name) = default_prompt_name.as_ref() {
+        match &prompts {
+            None => {
+                anyhow::bail!(format!("`default-prompt-name` is set to `{default_prompt_name}` but no prompts were found in the Sentence Transformers configuration"));
+            }
+            Some(prompts) if !prompts.contains_key(default_prompt_name) => {
+                anyhow::bail!(format!("`default-prompt-name` is set to `{default_prompt_name}` but it was not found in the Sentence Transformers prompts. Available prompts: {:?}", prompts.keys()));
+            }
+            Some(prompts) => prompts.get(default_prompt_name).cloned(),
+        }
+    } else {
+        default_prompt
+    };
+
     // Tokenization logic
     let tokenization = Tokenization::new(
         tokenization_workers,
         tokenizer,
         max_input_length,
         position_offset,
+        default_prompt,
+        prompts,
     );
 
     // Get dtype
@@ -198,6 +254,7 @@ pub async fn run(
         backend_model_type,
         uds_path.unwrap_or("/tmp/text-embeddings-inference-server".to_string()),
         otlp_endpoint.clone(),
+        otlp_service_name.clone(),
     )
     .context("Could not create backend")?;
     backend
@@ -216,9 +273,15 @@ pub async fn run(
         .or(max_batch_requests);
 
     // Warmup
-    backend.warmup(max_input_length as u32, max_batch_tokens as u32, max_batch_requests).await.context("Error when doing warmup")?;
-
-
+    if !backend.padded_model {
+        tracing::info!("Warming up model");
+        backend
+            .warmup(max_input_length as u32,
+                    max_batch_tokens as u32,
+                    max_batch_requests)
+            .await
+            .context("Error when doing warmup")?;
+    }
     // Queue logic
     let queue = Queue::new(
         backend.padded_model,
@@ -292,7 +355,7 @@ pub async fn run(
             api_key,
             cors_allow_origin,
         )
-        .await?;
+        .await
     }
 
     #[cfg(feature = "grpc")]
@@ -300,10 +363,8 @@ pub async fn run(
         // cors_allow_origin and payload_limit are not used for gRPC servers
         let _ = cors_allow_origin;
         let _ = payload_limit;
-        grpc::server::run(infer, info, addr, prom_builder, api_key).await?;
+        grpc::server::run(infer, info, addr, prom_builder, api_key).await
     }
-
-    Ok(())
 }
 
 fn get_backend_model_type(
@@ -341,13 +402,7 @@ fn get_backend_model_type(
             let config = fs::read_to_string(config_path).context("The `--pooling` arg is not set and we could not find a pooling configuration (`1_Pooling/config.json`) for this model.")?;
             let config: PoolConfig =
                 serde_json::from_str(&config).context("Failed to parse `1_Pooling/config.json`")?;
-            if config.pooling_mode_cls_token {
-                text_embeddings_backend::Pool::Cls
-            } else if config.pooling_mode_mean_tokens {
-                text_embeddings_backend::Pool::Mean
-            } else {
-                return Err(anyhow!("Pooling config {config:?} is not supported"));
-            }
+            Pool::try_from(config)?
         }
     };
     Ok(text_embeddings_backend::ModelType::Embedding(pool))
@@ -369,8 +424,25 @@ pub struct ModelConfig {
 pub struct PoolConfig {
     pooling_mode_cls_token: bool,
     pooling_mode_mean_tokens: bool,
-    pooling_mode_max_tokens: bool,
-    pooling_mode_mean_sqrt_len_tokens: bool,
+    #[serde(default)]
+    pooling_mode_lasttoken: bool,
+}
+
+impl TryFrom<PoolConfig> for Pool {
+    type Error = anyhow::Error;
+
+    fn try_from(config: PoolConfig) -> std::result::Result<Self, Self::Error> {
+        if config.pooling_mode_cls_token {
+            return Ok(Pool::Cls);
+        }
+        if config.pooling_mode_mean_tokens {
+            return Ok(Pool::Mean);
+        }
+        if config.pooling_mode_lasttoken {
+            return Ok(Pool::LastToken);
+        }
+        Err(anyhow!("Pooling config {config:?} is not supported"))
+    }
 }
 
 #[derive(Debug, Deserialize)]
@@ -378,6 +450,11 @@ pub struct STConfig {
     pub max_seq_length: usize,
 }
 
+#[derive(Debug, Deserialize)]
+pub struct NewSTConfig {
+    pub prompts: Option<HashMap<String, String>>,
+}
+
 #[derive(Clone, Debug, Serialize)]
 #[cfg_attr(feature = "http", derive(utoipa::ToSchema))]
 pub struct EmbeddingModel {
@@ -515,19 +592,14 @@ impl ResponseMetadata {
 
     fn record_metrics(&self) {
         // Metrics
-        metrics::histogram!(
-            "te_request_duration",
-            self.start_time.elapsed().as_secs_f64()
-        );
-        metrics::histogram!(
-            "te_request_tokenization_duration",
-            self.tokenization_time.as_secs_f64()
-        );
-        metrics::histogram!("te_request_queue_duration", self.queue_time.as_secs_f64());
-        metrics::histogram!(
-            "te_request_inference_duration",
-            self.inference_time.as_secs_f64()
-        );
+        let histogram = metrics::histogram!("te_request_duration");
+        histogram.record(self.start_time.elapsed().as_secs_f64());
+        let histogram = metrics::histogram!("te_request_tokenization_duration");
+        histogram.record(self.tokenization_time.as_secs_f64());
+        let histogram = metrics::histogram!("te_request_queue_duration");
+        histogram.record(self.queue_time.as_secs_f64());
+        let histogram = metrics::histogram!("te_request_inference_duration");
+        histogram.record(self.inference_time.as_secs_f64());
     }
 }
 
diff --git a/router/src/logging.rs b/router/src/logging.rs
index f8a6f0aa..7a8fe810 100644
--- a/router/src/logging.rs
+++ b/router/src/logging.rs
@@ -1,8 +1,8 @@
-use opentelemetry::sdk::propagation::TraceContextPropagator;
-use opentelemetry::sdk::trace::Sampler;
-use opentelemetry::sdk::{trace, Resource};
 use opentelemetry::{global, KeyValue};
 use opentelemetry_otlp::WithExportConfig;
+use opentelemetry_sdk::propagation::TraceContextPropagator;
+use opentelemetry_sdk::trace::Sampler;
+use opentelemetry_sdk::{trace, Resource};
 use tracing_subscriber::layer::SubscriberExt;
 use tracing_subscriber::util::SubscriberInitExt;
 use tracing_subscriber::{EnvFilter, Layer};
@@ -10,7 +10,11 @@ use tracing_subscriber::{EnvFilter, Layer};
 /// Init logging using env variables LOG_LEVEL and LOG_FORMAT:
 ///     - otlp_endpoint is an optional URL to an Open Telemetry collector
 ///     - LOG_LEVEL may be TRACE, DEBUG, INFO, WARN or ERROR (default to INFO)
-pub fn init_logging(otlp_endpoint: Option<&String>, json_output: bool) -> bool {
+pub fn init_logging(
+    otlp_endpoint: Option<&String>,
+    otlp_service_name: String,
+    json_output: bool,
+) -> bool {
     let mut layers = Vec::new();
 
     // STDOUT/STDERR layer
@@ -40,11 +44,11 @@ pub fn init_logging(otlp_endpoint: Option<&String>, json_output: bool) -> bool {
                 trace::config()
                     .with_resource(Resource::new(vec![KeyValue::new(
                         "service.name",
-                        "text-embeddings-inference.router",
+                        otlp_service_name,
                     )]))
                     .with_sampler(Sampler::AlwaysOn),
             )
-            .install_batch(opentelemetry::runtime::Tokio);
+            .install_batch(opentelemetry_sdk::runtime::Tokio);
 
         if let Ok(tracer) = tracer {
             layers.push(tracing_opentelemetry::layer().with_tracer(tracer).boxed());
diff --git a/router/src/main.rs b/router/src/main.rs
index 06cd576a..a72caa8b 100644
--- a/router/src/main.rs
+++ b/router/src/main.rs
@@ -1,12 +1,12 @@
 use anyhow::Result;
 use clap::Parser;
-use mimalloc::MiMalloc;
 use opentelemetry::global;
 use text_embeddings_backend::DType;
 use veil::Redact;
 
+#[cfg(not(target_os = "linux"))]
 #[global_allocator]
-static GLOBAL: MiMalloc = MiMalloc;
+static GLOBAL: mimalloc::MiMalloc = mimalloc::MiMalloc;
 
 /// App Configuration
 #[derive(Parser, Redact)]
@@ -79,6 +79,33 @@ struct Args {
     #[clap(long, env)]
     auto_truncate: bool,
 
+    /// The name of the prompt that should be used by default for encoding. If not set, no prompt
+    /// will be applied.
+    ///
+    /// Must be a key in the `Sentence Transformers` configuration `prompts` dictionary.
+    ///
+    /// For example if ``default_prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...},
+    /// then the sentence "What is the capital of France?" will be encoded as
+    /// "query: What is the capital of France?" because the prompt text will be prepended before
+    /// any text to encode.
+    ///
+    /// The argument '--default-prompt-name <DEFAULT_PROMPT_NAME>' cannot be used with
+    /// '--default-prompt <DEFAULT_PROMPT>`
+    #[clap(long, env, conflicts_with = "default_prompt")]
+    default_prompt_name: Option<String>,
+
+    /// The prompt that should be used by default for encoding. If not set, no prompt
+    /// will be applied.
+    ///
+    /// For example if ``default_prompt`` is "query: " then the sentence "What is the capital of
+    /// France?" will be encoded as "query: What is the capital of France?" because the prompt
+    /// text will be prepended before any text to encode.
+    ///
+    /// The argument '--default-prompt <DEFAULT_PROMPT>' cannot be used with
+    /// '--default-prompt-name <DEFAULT_PROMPT_NAME>`
+    #[clap(long, env, conflicts_with = "default_prompt_name")]
+    default_prompt: Option<String>,
+
     /// Your HuggingFace hub token
     #[clap(long, env)]
     #[redact(partial)]
@@ -123,6 +150,11 @@ struct Args {
     #[clap(long, env)]
     otlp_endpoint: Option<String>,
 
+    /// The service name for opentelemetry.
+    /// e.g. `text-embeddings-inference.server`
+    #[clap(default_value = "text-embeddings-inference.server", long, env)]
+    otlp_service_name: String,
+
     /// Unused for gRPC servers
     #[clap(long, env)]
     cors_allow_origin: Option<Vec<String>>,
@@ -134,11 +166,28 @@ async fn main() -> Result<()> {
     let args: Args = Args::parse();
 
     // Initialize logging and telemetry
-    let global_tracer =
-        text_embeddings_router::init_logging(args.otlp_endpoint.as_ref(), args.json_output);
+    let global_tracer = text_embeddings_router::init_logging(
+        args.otlp_endpoint.as_ref(),
+        args.otlp_service_name.clone(),
+        args.json_output,
+    );
 
     tracing::info!("{args:?}");
 
+    // Hack to trim pages regularly
+    // see: https://www.algolia.com/blog/engineering/when-allocators-are-hoarding-your-precious-memory/
+    // and: https://github.com/huggingface/text-embeddings-inference/issues/156
+    #[cfg(target_os = "linux")]
+    tokio::spawn(async move {
+        use tokio::time::Duration;
+        loop {
+            tokio::time::sleep(Duration::from_millis(100)).await;
+            unsafe {
+                libc::malloc_trim(0);
+            }
+        }
+    });
+
     text_embeddings_router::run(
         args.model_id,
         args.revision,
@@ -150,6 +199,8 @@ async fn main() -> Result<()> {
         args.max_batch_requests,
         args.max_client_batch_size,
         args.auto_truncate,
+        args.default_prompt,
+        args.default_prompt_name,
         args.hf_api_token,
         Some(args.hostname),
         args.port,
@@ -158,6 +209,7 @@ async fn main() -> Result<()> {
         args.payload_limit,
         args.api_key,
         args.otlp_endpoint,
+        args.otlp_service_name,
         args.cors_allow_origin,
     )
     .await?;
diff --git a/router/src/shutdown.rs b/router/src/shutdown.rs
index 471eaf14..15fc8ea7 100644
--- a/router/src/shutdown.rs
+++ b/router/src/shutdown.rs
@@ -25,5 +25,4 @@ pub(crate) async fn shutdown_signal() {
     }
 
     tracing::info!("signal received, starting graceful shutdown");
-    opentelemetry::global::shutdown_tracer_provider();
 }
diff --git a/router/tests/common.rs b/router/tests/common.rs
index f9c47c94..55fdf5f5 100644
--- a/router/tests/common.rs
+++ b/router/tests/common.rs
@@ -58,12 +58,15 @@ pub async fn start_server(model_id: String, revision: Option<String>, dtype: DTy
             false,
             None,
             None,
+            None,
+            None,
             8090,
             None,
             None,
             2_000_000,
             None,
             None,
+            "text-embeddings-inference.server".to_owned(),
             None,
         )
     });
diff --git a/sagemaker-entrypoint-cuda-all.sh b/sagemaker-entrypoint-cuda-all.sh
index 16156e8a..a3c63cbb 100644
--- a/sagemaker-entrypoint-cuda-all.sh
+++ b/sagemaker-entrypoint-cuda-all.sh
@@ -1,5 +1,26 @@
 #!/bin/bash
 
+verlte() {
+    [ "$1" = "$2" ] && return 1 || [ "$2" = "$(echo -e "$1\n$2" | sort -V | head -n1)" ]
+}
+
+if [ -f /usr/local/cuda/compat/libcuda.so.1 ]; then
+    CUDA_COMPAT_MAX_DRIVER_VERSION=$(readlink /usr/local/cuda/compat/libcuda.so.1 | cut -d"." -f 3-)
+    echo "CUDA compat package requires Nvidia driver ≤${CUDA_COMPAT_MAX_DRIVER_VERSION}"
+    cat /proc/driver/nvidia/version
+    NVIDIA_DRIVER_VERSION=$(sed -n 's/^NVRM.*Kernel Module *\([0-9.]*\).*$/\1/p' /proc/driver/nvidia/version 2>/dev/null || true)
+    echo "Current installed Nvidia driver version is ${NVIDIA_DRIVER_VERSION}"
+    if [ $(verlte "$CUDA_COMPAT_MAX_DRIVER_VERSION" "$NVIDIA_DRIVER_VERSION") ]; then
+        echo "Setup CUDA compatibility libs path to LD_LIBRARY_PATH"
+        export LD_LIBRARY_PATH=/usr/local/cuda/compat:$LD_LIBRARY_PATH
+        echo $LD_LIBRARY_PATH
+    else
+        echo "Skip CUDA compat libs setup as newer Nvidia driver is installed"
+    fi
+else
+    echo "Skip CUDA compat libs setup as package not found"
+fi
+
 if [[ -z "${HF_MODEL_ID}" ]]; then
   echo "HF_MODEL_ID must be set"
   exit 1
@@ -15,9 +36,43 @@ if ! command -v nvidia-smi &> /dev/null; then
     exit 1
 fi
 
+# Query GPU name using nvidia-smi
+gpu_name=$(nvidia-smi --query-gpu=gpu_name --format=csv | awk 'NR==2')
+if [ $? -ne 0 ]; then
+    echo "Error: $gpu_name"
+    echo "Query gpu_name failed"
+else
+    echo "Query gpu_name succeeded. Printing output: $gpu_name"
+fi
+
+# Function to get compute capability based on GPU name
+get_compute_cap() {
+    gpu_name="$1"
+
+    # Check if the GPU name contains "A10G"
+    if [[ "$gpu_name" == *"A10G"* ]]; then
+        echo "86"
+    # Check if the GPU name contains "A100"
+    elif [[ "$gpu_name" == *"A100"* ]]; then
+        echo "80"
+    # Check if the GPU name contains "H100"
+    elif [[ "$gpu_name" == *"H100"* ]]; then
+        echo "90"
+    # Cover Nvidia T4
+    elif [[ "$gpu_name" == *"T4"* ]]; then
+        echo "75"
+    # Cover Nvidia L4
+    elif [[ "$gpu_name" == *"L4"* ]]; then
+        echo "89"
+    else
+        echo "80"  # Default compute capability
+    fi
+}
+
 if [[ -z "${CUDA_COMPUTE_CAP}" ]]
 then
-    compute_cap=$(nvidia-smi --query-gpu=compute_cap --format=csv | sed -n '2p' | sed 's/\.//g')
+    compute_cap=$(get_compute_cap "$gpu_name")
+    echo "the compute_cap is $compute_cap"
 else
     compute_cap=$CUDA_COMPUTE_CAP
 fi
diff --git a/sagemaker-entrypoint.sh b/sagemaker-entrypoint.sh
index 8d78a8dc..b6cec7bb 100644
--- a/sagemaker-entrypoint.sh
+++ b/sagemaker-entrypoint.sh
@@ -10,4 +10,4 @@ if [[ -n "${HF_MODEL_REVISION}" ]]; then
   export REVISION="${HF_MODEL_REVISION}"
 fi
 
-text-embeddings-router --port 8080 --json-output
\ No newline at end of file
+text-embeddings-router --port 8080 --json-output