Instructions to use aws-neuron/Mistral-neuron with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aws-neuron/Mistral-neuron with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aws-neuron/Mistral-neuron")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aws-neuron/Mistral-neuron") model = AutoModelForCausalLM.from_pretrained("aws-neuron/Mistral-neuron") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use aws-neuron/Mistral-neuron with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aws-neuron/Mistral-neuron" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aws-neuron/Mistral-neuron", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aws-neuron/Mistral-neuron
- SGLang
How to use aws-neuron/Mistral-neuron 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 "aws-neuron/Mistral-neuron" \ --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": "aws-neuron/Mistral-neuron", "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 "aws-neuron/Mistral-neuron" \ --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": "aws-neuron/Mistral-neuron", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aws-neuron/Mistral-neuron with Docker Model Runner:
docker model run hf.co/aws-neuron/Mistral-neuron
- Xet hash:
- da7b1981544974d7ad54a95f3420e3102845eca83c9f0e5b341da29af44aa7c7
- Size of remote file:
- 235 MB
- SHA256:
- a785f23db614522b2167cf41f019aaa3704c3cfc002ed3aa1fb5d0c4fd6e5e1f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.