diff --git a/torchbenchmark/canary_models/hf_mixtral/__init__.py b/torchbenchmark/canary_models/hf_mixtral/__init__.py
new file mode 100644
index 0000000000..6d91f8f86a
--- /dev/null
+++ b/torchbenchmark/canary_models/hf_mixtral/__init__.py
@@ -0,0 +1,17 @@
+from torchbenchmark.tasks import NLP
+from torchbenchmark.util.framework.huggingface.model_factory import HuggingFaceModel
+
+class Model(HuggingFaceModel):
+    task = NLP.LANGUAGE_MODELING
+    # DEFAULT_TRAIN_BSIZE not specified since we're not implementing a train test
+    # DEFAULT_TRAIN_BSIZE = 1
+    DEFAULT_EVAL_BSIZE = 1
+
+    def __init__(self, test, device, batch_size=None, extra_args=[]):
+        super().__init__(name="hf_mixtral", test=test, device=device, batch_size=batch_size, extra_args=extra_args)
+
+    # # def train(self):
+    # #     return NotImplementedError("Not implemented")
+
+    # def eval(self):
+    #     super().eval()
\ No newline at end of file
diff --git a/torchbenchmark/canary_models/hf_mixtral/install.py b/torchbenchmark/canary_models/hf_mixtral/install.py
new file mode 100644
index 0000000000..64e5b1127e
--- /dev/null
+++ b/torchbenchmark/canary_models/hf_mixtral/install.py
@@ -0,0 +1,13 @@
+import subprocess
+import sys
+import os
+from torchbenchmark.util.framework.huggingface.patch_hf import patch_transformers, cache_model
+
+def pip_install_requirements():
+    subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt'])
+
+if __name__ == '__main__':
+    pip_install_requirements()
+    patch_transformers()
+    model_name = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
+    cache_model(model_name, trust_remote_code=True)
\ No newline at end of file
diff --git a/torchbenchmark/canary_models/hf_mixtral/metadata.yaml b/torchbenchmark/canary_models/hf_mixtral/metadata.yaml
new file mode 100644
index 0000000000..19877db021
--- /dev/null
+++ b/torchbenchmark/canary_models/hf_mixtral/metadata.yaml
@@ -0,0 +1,11 @@
+devices:
+  NVIDIA A100-SXM4-40GB:
+    eval_batch_size: 1
+eval_benchmark: false
+eval_deterministic: false
+eval_nograd: true
+train_benchmark: false
+train_deterministic: false
+not_implemented:
+  - device: NVIDIA A10G
+  # - device: cpu
\ No newline at end of file
diff --git a/torchbenchmark/canary_models/hf_mixtral/requirements.txt b/torchbenchmark/canary_models/hf_mixtral/requirements.txt
new file mode 100644
index 0000000000..5d54ada424
--- /dev/null
+++ b/torchbenchmark/canary_models/hf_mixtral/requirements.txt
@@ -0,0 +1,3 @@
+bitsandbytes
+transformers>=4.36.2
+numba
\ No newline at end of file
diff --git a/torchbenchmark/util/framework/huggingface/model_factory.py b/torchbenchmark/util/framework/huggingface/model_factory.py
index 4d23251999..c32c4c54dc 100644
--- a/torchbenchmark/util/framework/huggingface/model_factory.py
+++ b/torchbenchmark/util/framework/huggingface/model_factory.py
@@ -29,6 +29,7 @@
     'hf_Bert_large': (512, 512, 'BertConfig(hidden_size=1024, num_hidden_layers=24, num_attention_heads=16)', 'AutoModelForMaskedLM'),
     'hf_Whisper': (1024, 1024, 'WhisperConfig()', 'AutoModelForAudioClassification'),
     'hf_distil_whisper': (1024, 1024, 'AutoConfig.from_pretrained("distil-whisper/distil-medium.en")', 'AutoModelForAudioClassification'),
+    'hf_mixtral' : (512,512, 'AutoConfig.from_pretrained("mistralai/Mixtral-8x7B-v0.1")', 'AutoModelForCausalLM'),
     # default num_hidden_layers=32 but that OOMs, feel free to change this config to something more real
     'llama_v2_7b_16h' : (128,512, 'LlamaConfig(num_hidden_layers=16)', 'AutoModelForCausalLM'),
     'hf_MPT_7b_instruct': (512, 512, 'AutoConfig.from_pretrained("mosaicml/mpt-7b-instruct", trust_remote_code=True)', 'AutoModelForCausalLM'),
@@ -39,7 +40,6 @@
     # as per this page https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 trust_remote_code=True is not required
     'mistral_7b_instruct' : (128, 128, 'AutoConfig.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")', 'AutoModelForCausalLM'),
     'hf_Yi' : (512, 512, 'AutoConfig.from_pretrained("01-ai/Yi-6B", trust_remote_code=True)', 'AutoModelForCausalLM'),
-
 }
 
 cpu_input_slice = {