Instructions to use mistralai/Mistral-7B-Instruct-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mistralai/Mistral-7B-Instruct-v0.2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mistralai/Mistral-7B-Instruct-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Install mistral-common:
pip install --upgrade mistral-common
# Start the vLLM server:
vllm serve "mistralai/Mistral-7B-Instruct-v0.2" --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mistralai/Mistral-7B-Instruct-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mistralai/Mistral-7B-Instruct-v0.2

SGLang

How to use mistralai/Mistral-7B-Instruct-v0.2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mistralai/Mistral-7B-Instruct-v0.2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mistralai/Mistral-7B-Instruct-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mistralai/Mistral-7B-Instruct-v0.2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mistralai/Mistral-7B-Instruct-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mistralai/Mistral-7B-Instruct-v0.2 with Docker Model Runner:
```
docker model run hf.co/mistralai/Mistral-7B-Instruct-v0.2
```

Unable to Access Mistral-7B-Instruct-v0.2 Model

#159

by charudatta-17 - opened Sep 13, 2024

Discussion

charudatta-17

Sep 13, 2024

I have been using the mistralai/Mistral-7B-Instruct-v0.2 model for a case study, and it was functioning perfectly until recently. However, I am now encountering an issue where I am unable to access the model. The error message I receive is as follows:

Model not loaded on the server: https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2. Please retry with a higher timeout (current: 120).

HF Team, Could you please provide any suggestions or alternatives so that I can access this model again?

nanidrita

Sep 13, 2024

Yes, I am also getting the same issue. I tried with llm = HuggingFaceEndpoint(timeout=400, repo_id="mistralai/Mistral-7B-Instruct-v0.2") but no luck

akamalov

Sep 14, 2024

Same. I am getting the following error, although I have been authenticated using huggingface-cli login

raise ModelNotFoundError(

mlx_lm.utils.ModelNotFoundError: Model not found for path or HF repo: mistralai/Mistral-7B-Instruct-v0.2.
Please make sure you specified the local path or Hugging Face repo id correctly.
If you are trying to access a private or gated Hugging Face repo, make sure you are authenticated:
https://huggingface.co/docs/huggingface_hub/en/guides/cli#huggingface-cli-login

becodev

Sep 15, 2024

I am getting a similar issue.
InferenceTimeoutError: Model not loaded on the server: https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2. Please retry with a higher timeout (current: 120)

akamalov

Sep 15, 2024

What can we do to escalate this issue ?

kidViking

Mar 22, 2025

It has restricted access now.

Cannot access gated repo for url https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2/resolve/3ad372fc79158a2148299e3318516c786aeded6c/.gitattributes.
Access to model mistralai/Mistral-7B-Instruct-v0.2 is restricted and you are not in the authorized list. Visit https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2 to ask for access.

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