Text Generation
Transformers
PyTorch
Safetensors
English
mistral
pretrained
mistral-common
Eval Results
text-generation-inference
Instructions to use mistralai/Mistral-7B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mistralai/Mistral-7B-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mistralai/Mistral-7B-v0.1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") - Inference
- Local Apps Settings
- vLLM
How to use mistralai/Mistral-7B-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-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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mistralai/Mistral-7B-v0.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mistralai/Mistral-7B-v0.1
- SGLang
How to use mistralai/Mistral-7B-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-v0.1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mistralai/Mistral-7B-v0.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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-v0.1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mistralai/Mistral-7B-v0.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mistralai/Mistral-7B-v0.1 with Docker Model Runner:
docker model run hf.co/mistralai/Mistral-7B-v0.1
Commit ·
b3d8f12
1
Parent(s): f966a60
Upload tokenizer
Browse files- special_tokens_map.json +5 -0
- tokenizer.json +3 -3
- tokenizer_config.json +5 -1
special_tokens_map.json
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"bos_token": "<s>",
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tokenizer.json
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tokenizer_config.json
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