Instructions to use mistralai/Mistral-7B-Instruct-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mistralai/Mistral-7B-Instruct-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.1") 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-v0.1") model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") 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-v0.1 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-v0.1" --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-v0.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mistralai/Mistral-7B-Instruct-v0.1
- SGLang
How to use mistralai/Mistral-7B-Instruct-v0.1 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-v0.1" \ --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-v0.1", "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-v0.1" \ --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-v0.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mistralai/Mistral-7B-Instruct-v0.1 with Docker Model Runner:
docker model run hf.co/mistralai/Mistral-7B-Instruct-v0.1
What is the context size of this model?
Is it 4k or 8k?
It’s 8k context
is the inference context length also 8K or can it go higher?
With sliding window I think it can reach 32k as it says but normally it’s around 7 to 8k
With sliding window I think it can reach 32k as it says but normally it’s around 7 to 8k
Please cite a source :)
With sliding window I think it can reach 32k as it says but normally it’s around 7 to 8k
Please cite a source :)
Here, official site, it states 8K for the base model. I am not sure if that could have been changed for the Instruct version.
https://mistral.ai/product/
Thanks :)