Instructions to use mlx-community/Mistral-7B-Instruct-v0.2-8-bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Mistral-7B-Instruct-v0.2-8-bit-mlx with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("mlx-community/Mistral-7B-Instruct-v0.2-8-bit-mlx") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use mlx-community/Mistral-7B-Instruct-v0.2-8-bit-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mlx-community/Mistral-7B-Instruct-v0.2-8-bit-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/Mistral-7B-Instruct-v0.2-8-bit-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/Mistral-7B-Instruct-v0.2-8-bit-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }'
Mistral-7B-Instruct-v0.2-8-bit-mlx
This model was converted to MLX format from mistralai/Mistral-7B-Instruct-v0.2.
Refer to the original model card for more details on the model.
Use with mlx
pip install mlx
git clone https://github.com/ml-explore/mlx-examples.git
cd mlx-examples/llms/hf_llm
python generate.py --model mlx-community/Mistral-7B-Instruct-v0.2-8-bit-mlx --prompt "My name is"
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