gemma.cpp

gemma.cpp is a lightweight, standalone C++ inference engine for the Gemma foundation models from Google.

For additional information about Gemma, see ai.google.dev/gemma. Model weights, including gemma.cpp specific artifacts, are available on kaggle.

NOTE: 2024-04-04: if using 2B models, please re-download weights from Kaggle and ensure you have the latest version (-mqa or version 3). We are changing the code to match the new weights. If you wish to use old weights, change ConfigGemma2B in configs.h back to kVocabSize = 256128 and kKVHeads = 8.

Who is this project for?

Modern LLM inference engines are sophisticated systems, often with bespoke capabilities extending beyond traditional neural network runtimes. With this comes opportunities for research and innovation through co-design of high level algorithms and low-level computation. However, there is a gap between deployment-oriented C++ inference runtimes, which are not designed for experimentation, and Python-centric ML research frameworks, which abstract away low-level computation through compilation.

gemma.cpp provides a minimalist implementation of Gemma 2B and 7B models, focusing on simplicity and directness rather than full generality. This is inspired by vertically-integrated model implementations such as ggml, llama.c, and llama.rs.

gemma.cpp targets experimentation and research use cases. It is intended to be straightforward to embed in other projects with minimal dependencies and also easily modifiable with a small ~2K LoC core implementation (along with ~4K LoC of supporting utilities). We use the Google Highway Library to take advantage of portable SIMD for CPU inference.

For production-oriented edge deployments we recommend standard deployment pathways using Python frameworks like JAX, Keras, PyTorch, and Transformers (all model variations here).

Contributing

Community contributions large and small are welcome. See DEVELOPERS.md for additional notes contributing developers and join the discord by following this invite link. This project follows Google's Open Source Community Guidelines.

Active development is currently done on the dev branch. Please open pull requests targeting dev branch instead of main, which is intended to be more stable.

Quick Start

System requirements

Before starting, you should have installed:

• CMake
• Clang C++ compiler, supporting at least C++17.
• tar for extracting archives from Kaggle.

Building natively on Windows requires the Visual Studio 2012 Build Tools with the optional Clang/LLVM C++ frontend (clang-cl). This can be installed from the command line with winget:

winget install --id Kitware.CMake
winget install --id Microsoft.VisualStudio.2022.BuildTools --force --override "--passive --wait --add Microsoft.VisualStudio.Workload.VCTools;installRecommended --add Microsoft.VisualStudio.Component.VC.Llvm.Clang --add Microsoft.VisualStudio.Component.VC.Llvm.ClangToolset"

Step 1: Obtain model weights and tokenizer from Kaggle or Hugging Face Hub

Visit the Gemma model page on Kaggle and select Model Variations |> Gemma C++. On this tab, the Variation dropdown includes the options below. Note bfloat16 weights are higher fidelity, while 8-bit switched floating point weights enable faster inference. In general, we recommend starting with the -sfp checkpoints.

Alternatively, visit the gemma.cpp models on the Hugging Face Hub. First go the the model repository of the model of interest (see recommendations below). Then, click the Files and versions tab and download the model and tokenizer files. For programmatic downloading, if you have huggingface_hub installed, you can also download by running:

huggingface-cli login # Just the first time
huggingface-cli download google/gemma-2b-sfp-cpp --local-dir build/

2B instruction-tuned (it) and pre-trained (pt) models:

Model name	Description
2b-it	2 billion parameter instruction-tuned model, bfloat16
2b-it-sfp	2 billion parameter instruction-tuned model, 8-bit switched floating point
2b-pt	2 billion parameter pre-trained model, bfloat16
2b-pt-sfp	2 billion parameter pre-trained model, 8-bit switched floating point

7B instruction-tuned (it) and pre-trained (pt) models:

Model name	Description
7b-it	7 billion parameter instruction-tuned model, bfloat16
7b-it-sfp	7 billion parameter instruction-tuned model, 8-bit switched floating point
7b-pt	7 billion parameter pre-trained model, bfloat16
7b-pt-sfp	7 billion parameter pre-trained model, 8-bit switched floating point

Note

Important: We strongly recommend starting off with the 2b-it-sfp model to get up and running.

Step 2: Extract Files

If you downloaded the models from Hugging Face, skip to step 3.

After filling out the consent form, the download should proceed to retrieve a tar archive file archive.tar.gz. Extract files from archive.tar.gz (this can take a few minutes):

tar -xf archive.tar.gz

This should produce a file containing model weights such as 2b-it-sfp.sbs and a tokenizer file (tokenizer.spm). You may want to move these files to a convenient directory location (e.g. the build/ directory in this repo).

Step 3: Build

The build system uses CMake. To build the gemma inference runtime, create a build directory and generate the build files using cmake from the top-level project directory. Note if you previous ran cmake and are re-running with a different setting, be sure to delete all files in the build/ directory with rm -rf build/*.

Unix-like Platforms

cmake -B build

After running cmake, you can enter the build/ directory and run make to build the ./gemma executable:

# Configure `build` directory
cmake --preset make

# Build project using make
cmake --build --preset make -j [number of parallel threads to use]

Replace [number of parallel threads to use] with a number - the number of cores available on your system is a reasonable heuristic. For example, make -j4 gemma will build using 4 threads. If the nproc command is available, you can use make -j$(nproc) gemma as a reasonable default for the number of threads.

If you aren't sure of the right value for the -j flag, you can simply run make gemma instead and it should still build the ./gemma executable.

Note

On Windows Subsystem for Linux (WSL) users should set the number of parallel threads to 1. Using a larger number may result in errors.

If the build is successful, you should now have a gemma executable in the build/ directory.

Windows

# Configure `build` directory
cmake --preset windows

# Build project using Visual Studio Build Tools
cmake --build --preset windows -j [number of parallel threads to use]

If the build is successful, you should now have a gemma.exe executable in the build/ directory.

Bazel

bazel build -c opt --cxxopt=-std=c++20 :gemma

If the build is successful, you should now have a gemma executable in the bazel-bin/ directory.

Make

If you prefer Makefiles, @jart has made one available here:

https://github.com/jart/gemma3/blob/main/Makefile

Step 4: Run

You can now run gemma from inside the build/ directory.

gemma has the following required arguments:

Argument	Description	Example value
--model	The model type.	2b-it ... (see below)
--weights	The compressed weights file.	2b-it-sfp.sbs
--weight_type	The compressed weight type.	sfp
--tokenizer	The tokenizer file.	tokenizer.spm

gemma is invoked as:

./gemma \
--tokenizer [tokenizer file] \
--weights [compressed weights file] \
--weight_type [f32 or bf16 or sfp] \
--model [2b-it or 2b-pt or 7b-it or 7b-pt or ...]

Example invocation for the following configuration:

• Compressed weights file 2b-it-sfp.sbs (2B instruction-tuned model, 8-bit switched floating point).
• Tokenizer file tokenizer.spm.

./gemma \
--tokenizer tokenizer.spm \
--weights 2b-it-sfp.sbs --weight_type sfp --model 2b-it

RecurrentGemma

This repository includes a version of Gemma based on Griffin (paper, code). Its architecture includes both recurrent layers and local attention, thus it is more efficient for longer sequences and has a smaller memory footprint than standard Gemma. We here provide a C++ implementation of this model based on the paper.

To use the recurrent version of Gemma included in this repository, build the gemma binary as noted above in Step 3. Download the compressed weights and tokenizer from the RecurrentGemma Kaggle as in Step 1, and run the binary as follows:

./gemma --tokenizer tokenizer.spm --model gr2b-it --weights 2b-it-sfp.sbs

Troubleshooting and FAQs

Running ./gemma fails with "Failed to read cache gating_ein_0 (error 294) ..."

The most common problem is that the --weight_type argument does not match that of the model file. Revisit step #3 and check which weights you downloaded.

Note that we have already moved weight type from a compile-time decision to a runtime argument. In a subsequent step, we plan to bake this information into the weights.

Problems building in Windows / Visual Studio

Currently if you're using Windows, we recommend building in WSL (Windows Subsystem for Linux). We are exploring options to enable other build configurations, see issues for active discussion.

Model does not respond to instructions and produces strange output

A common issue is that you are using a pre-trained model, which is not instruction-tuned and thus does not respond to instructions. Make sure you are using an instruction-tuned model (2b-it-sfp, 2b-it, 7b-it-sfp, 7b-it) and not a pre-trained model (any model with a -pt suffix).

How do I convert my fine-tune to a .sbs compressed model file?

We're working on a python script to convert a standard model format to .sbs, and hope have it available in the next week or so. Follow this issue for updates.

What are some easy ways to make the model run faster?

1. Make sure you are using the 8-bit switched floating point -sfp models.
2. If you're on a laptop, make sure power mode is set to maximize performance and saving mode is off. For most laptops, the power saving modes get activated automatically if the computer is not plugged in.
3. Close other unused cpu-intensive applications.
4. On macs, anecdotally we observe a "warm-up" ramp-up in speed as performance cores get engaged.
5. Experiment with the --num_threads argument value. Depending on the device, larger numbers don't always mean better performance.

We're also working on algorithmic and optimization approaches for faster inference, stay tuned.

Usage

gemma has different usage modes, controlled by the verbosity flag.

All usage modes are currently interactive, triggering text generation upon newline input.

Verbosity	Usage mode	Details
--verbosity 0	Minimal	Only prints generation output. Suitable as a CLI tool.
--verbosity 1	Default	Standard user-facing terminal UI.
--verbosity 2	Detailed	Shows additional developer and debug info.

Interactive Terminal App

By default, verbosity is set to 1, bringing up a terminal-based interactive interface when gemma is invoked:

$ ./gemma [...]
  __ _  ___ _ __ ___  _ __ ___   __ _   ___ _ __  _ __
 / _` |/ _ \ '_ ` _ \| '_ ` _ \ / _` | / __| '_ \| '_ \
| (_| |  __/ | | | | | | | | | | (_| || (__| |_) | |_) |
 \__, |\___|_| |_| |_|_| |_| |_|\__,_(_)___| .__/| .__/
  __/ |                                    | |   | |
 |___/                                     |_|   |_|

tokenizer                     : tokenizer.spm
compressed_weights            : 2b-it-sfp.sbs
model                         : 2b-it
weights                       : [no path specified]
max_tokens                    : 3072
max_generated_tokens          : 2048

*Usage*
  Enter an instruction and press enter (%C reset conversation, %Q quits).

*Examples*
  - Write an email to grandma thanking her for the cookies.
  - What are some historical attractions to visit around Massachusetts?
  - Compute the nth fibonacci number in javascript.
  - Write a standup comedy bit about WebGPU programming.

> What are some outdoorsy places to visit around Boston?

[ Reading prompt ] .....................


**Boston Harbor and Islands:**

* **Boston Harbor Islands National and State Park:** Explore pristine beaches, wildlife, and maritime history.
* **Charles River Esplanade:** Enjoy scenic views of the harbor and city skyline.
* **Boston Harbor Cruise Company:** Take a relaxing harbor cruise and admire the city from a different perspective.
* **Seaport Village:** Visit a charming waterfront area with shops, restaurants, and a seaport museum.

**Forest and Nature:**

* **Forest Park:** Hike through a scenic forest with diverse wildlife.
* **Quabbin Reservoir:** Enjoy boating, fishing, and hiking in a scenic setting.
* **Mount Forest:** Explore a mountain with breathtaking views of the city and surrounding landscape.

...

Usage as a Command Line Tool

For using the gemma executable as a command line tool, it may be useful to create an alias for gemma.cpp with arguments fully specified:

alias gemma2b="~/gemma.cpp/build/gemma -- --tokenizer ~/gemma.cpp/build/tokenizer.spm --weights ~/gemma.cpp/build/2b-it-sfp.sbs --model 2b-it --verbosity 0"

Replace the above paths with your own paths to the model and tokenizer paths from the download.

Here is an example of prompting gemma with a truncated input file (using a gemma2b alias like defined above):

cat configs.h | tail -35 | tr '\n' ' ' | xargs -0 echo "What does this C++ code do: " | gemma2b

Note

CLI usage of gemma.cpp is experimental and should take context length limitations into account.

The output of the above command should look like:

$ cat configs.h | tail -35 | tr '\n' ' ' | xargs -0 echo "What does this C++ code do: " | gemma2b
[ Reading prompt ] ......................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
The code defines two C++ structs, `ConfigGemma7B` and `ConfigGemma2B`, which are used for configuring a deep learning model.

**ConfigGemma7B**:

* `kSeqLen`: Stores the length of the sequence to be processed. It's set to 7168.
* `kVocabSize`: Stores the size of the vocabulary, which is 256128.
* `kLayers`: Number of layers in the deep learning model. It's set to 28.
* `kModelDim`: Dimension of the model's internal representation. It's set to 3072.
* `kFFHiddenDim`: Dimension of the feedforward and recurrent layers' hidden representations. It's set to 16 * 3072 / 2.

**ConfigGemma2B**:

* `kSeqLen`: Stores the length of the sequence to be processed. It's also set to 7168.
* `kVocabSize`: Size of the vocabulary, which is 256128.
* `kLayers`: Number of layers in the deep learning model. It's set to 18.
* `kModelDim`: Dimension of the model's internal representation. It's set to 2048.
* `kFFHiddenDim`: Dimension of the feedforward and recurrent layers' hidden representations. It's set to 16 * 2048 / 2.

These structs are used to configure a deep learning model with specific parameters for either Gemma7B or Gemma2B architecture.

Incorporating gemma.cpp as a Library in your Project

The easiest way to incorporate gemma.cpp in your own project is to pull in gemma.cpp and dependencies using FetchContent. You can add the following to your CMakeLists.txt:

include(FetchContent)

FetchContent_Declare(sentencepiece GIT_REPOSITORY https://github.com/google/sentencepiece GIT_TAG 53de76561cfc149d3c01037f0595669ad32a5e7c)
FetchContent_MakeAvailable(sentencepiece)

FetchContent_Declare(gemma GIT_REPOSITORY https://github.com/google/gemma.cpp GIT_TAG origin/main)
FetchContent_MakeAvailable(gemma)

FetchContent_Declare(highway GIT_REPOSITORY https://github.com/google/highway.git GIT_TAG da250571a45826b21eebbddc1e50d0c1137dee5f)
FetchContent_MakeAvailable(highway)

Note for the gemma.cpp GIT_TAG, you may replace origin/main for a specific commit hash if you would like to pin the library version.

After your executable is defined (substitute your executable name for [Executable Name] below):

target_link_libraries([Executable Name] libgemma hwy hwy_contrib sentencepiece)
FetchContent_GetProperties(gemma)
FetchContent_GetProperties(sentencepiece)
target_include_directories([Executable Name] PRIVATE ${gemma_SOURCE_DIR})
target_include_directories([Executable Name] PRIVATE ${sentencepiece_SOURCE_DIR})

Building gemma.cpp as a Library

gemma.cpp can also be used as a library dependency in your own project. The shared library artifact can be built by modifying the make invocation to build the libgemma target instead of gemma.

Note

If you are using gemma.cpp in your own project with the FetchContent steps in the previous section, building the library is done automatically by cmake and this section can be skipped.

First, run cmake:

cmake -B build

Then, run make with the libgemma target:

cd build
make -j [number of parallel threads to use] libgemma

If this is successful, you should now have a libgemma library file in the build/ directory. On Unix platforms, the filename is libgemma.a.

Independent Projects Using gemma.cpp

Some independent projects using gemma.cpp:

• gemma-cpp-python - Python bindings
• lua-cgemma - Lua bindings
• Godot engine demo project

If you would like to have your project included, feel free to get in touch or submit a PR with a README.md edit.

Acknowledgements and Contacts

gemma.cpp was started in fall 2023 by Austin Huang and Jan Wassenberg, and subsequently released February 2024 thanks to contributions from Phil Culliton, Paul Chang, and Dan Zheng.

Griffin support was implemented in April 2024 thanks to contributions by Andrey Mikhaylov, Eugene Kliuchnikov, Jan Wassenberg, Jyrki Alakuijala, Lode Vandevenne, Luca Versari, Martin Bruse, Phil Culliton, Sami Boukortt, Thomas Fischbacher and Zoltan Szabadka.

This is not an officially supported Google product.