Text Generation
Transformers
PyTorch
Safetensors
English
mistral
pretrained
text-generation-inference
Instructions to use Intrinsic-Data/mistral7b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intrinsic-Data/mistral7b-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Intrinsic-Data/mistral7b-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Intrinsic-Data/mistral7b-base") model = AutoModelForCausalLM.from_pretrained("Intrinsic-Data/mistral7b-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Intrinsic-Data/mistral7b-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Intrinsic-Data/mistral7b-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Intrinsic-Data/mistral7b-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Intrinsic-Data/mistral7b-base
- SGLang
How to use Intrinsic-Data/mistral7b-base 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 "Intrinsic-Data/mistral7b-base" \ --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": "Intrinsic-Data/mistral7b-base", "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 "Intrinsic-Data/mistral7b-base" \ --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": "Intrinsic-Data/mistral7b-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Intrinsic-Data/mistral7b-base with Docker Model Runner:
docker model run hf.co/Intrinsic-Data/mistral7b-base
Commit History
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Adding `safetensors` variant of this model (#101)
- Adding `safetensors` variant of this model (c64c948c7796ad50663a0a9baee78cca47fa9cd9) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>26bca36
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Update tokenizer_config.json
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Add language (#56)
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added arxiv
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Remove `added_tokens` json file (#44)
- remove added_tokens file (cfffbe0c83941ba77ccdec416e0104721a48f4a7) Co-authored-by: Lucile Saulnier <SaulLu@users.noreply.huggingface.co>1fb9932
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Clarify Transformers version requirements
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Upload tokenizer (#36)
- Upload tokenizer (b3d8f126fcbfffb74c7fb0c809d85e68818c598d) - Update tokenizer_config.json (a135a5d59f4fe14077d590ecf038e69f1f11cb30) - Update special_tokens_map.json (ac708c38a76471069879472a48ce9d789b8cfa06) - Update special_tokens_map.json (9cabe799106feebabee9802a785b8c7d36c8a464)f801b4a
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Disable normalization for special tokens (#26)
- Disable normalization for special tokens (5f9cf2a1989fdcad773ff16c3177e0711f895d38) Co-authored-by: Lewis Tunstall <lewtun@users.noreply.huggingface.co>f966a60 -
Update inference temperature
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Update README.md (#15)
- Update README.md (8959b31e520b808acd9ad08db60880e5b8677b85) Co-authored-by: Devendra Singh Chaplot <devendrachaplot@users.noreply.huggingface.co>78814a9 -
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Upload folder using huggingface_hub
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Small updates
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initial commit
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