Instructions to use alexsherstinsky/Mistral-7B-v0.1-sharded with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alexsherstinsky/Mistral-7B-v0.1-sharded with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="alexsherstinsky/Mistral-7B-v0.1-sharded")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("alexsherstinsky/Mistral-7B-v0.1-sharded") model = AutoModelForCausalLM.from_pretrained("alexsherstinsky/Mistral-7B-v0.1-sharded", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use alexsherstinsky/Mistral-7B-v0.1-sharded with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "alexsherstinsky/Mistral-7B-v0.1-sharded" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alexsherstinsky/Mistral-7B-v0.1-sharded", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/alexsherstinsky/Mistral-7B-v0.1-sharded
- SGLang
How to use alexsherstinsky/Mistral-7B-v0.1-sharded 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 "alexsherstinsky/Mistral-7B-v0.1-sharded" \ --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": "alexsherstinsky/Mistral-7B-v0.1-sharded", "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 "alexsherstinsky/Mistral-7B-v0.1-sharded" \ --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": "alexsherstinsky/Mistral-7B-v0.1-sharded", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use alexsherstinsky/Mistral-7B-v0.1-sharded with Docker Model Runner:
docker model run hf.co/alexsherstinsky/Mistral-7B-v0.1-sharded
- Xet hash:
- 64ec0101fba7d5fafb0624f3e0899c76c270d54aea2e74b552e50e7d4f3f6013
- Size of remote file:
- 1.98 GB
- SHA256:
- 2f018e94486f25c0496dd394596db202b34f3337db76a8327f2c656d92140d86
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