Instructions to use Vezora/Mistral-22B-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vezora/Mistral-22B-v0.2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Vezora/Mistral-22B-v0.2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Vezora/Mistral-22B-v0.2") model = AutoModelForCausalLM.from_pretrained("Vezora/Mistral-22B-v0.2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Vezora/Mistral-22B-v0.2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Vezora/Mistral-22B-v0.2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vezora/Mistral-22B-v0.2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Vezora/Mistral-22B-v0.2
- SGLang
How to use Vezora/Mistral-22B-v0.2 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 "Vezora/Mistral-22B-v0.2" \ --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": "Vezora/Mistral-22B-v0.2", "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 "Vezora/Mistral-22B-v0.2" \ --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": "Vezora/Mistral-22B-v0.2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Vezora/Mistral-22B-v0.2 with Docker Model Runner:
docker model run hf.co/Vezora/Mistral-22B-v0.2
How do you compress a MOE in a single dense model ?
Hello,
what you do is wonderful! Can you tell me how do you compress a MOE in a single dense model ? Do you want to share some colab with that ? I have in mind some MOE that I would love to try to compress it myself ! Thank you if you want to share info and help ! You are the great !
I would be grateful if you could share the script you got from Charles Coddard that helped you make this, I'd love to learn more!
https://huggingface.co/thomasgauthier/Unmixtraled-22B-v0.1-expert-2
This is a 22B Mistral model recycling weights from mistral-community/Mixtral-8x22B-v0.1. The model was adapted from a Mixtral architecture to a dense Mistral architecture with the same number of layers, attention heads and hidden dimensions, and you might find it interesting.