Instructions to use shamsghi/Mistral-Le-Chaton-Fat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use shamsghi/Mistral-Le-Chaton-Fat 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("shamsghi/Mistral-Le-Chaton-Fat") 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
- Pi
How to use shamsghi/Mistral-Le-Chaton-Fat with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "shamsghi/Mistral-Le-Chaton-Fat"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "shamsghi/Mistral-Le-Chaton-Fat" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use shamsghi/Mistral-Le-Chaton-Fat with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "shamsghi/Mistral-Le-Chaton-Fat"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default shamsghi/Mistral-Le-Chaton-Fat
Run Hermes
hermes
- MLX LM
How to use shamsghi/Mistral-Le-Chaton-Fat with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "shamsghi/Mistral-Le-Chaton-Fat"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "shamsghi/Mistral-Le-Chaton-Fat" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shamsghi/Mistral-Le-Chaton-Fat", "messages": [ {"role": "user", "content": "Hello"} ] }'
Update README.md
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README.md
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Le Chaton Fat launches today alongside Le Chaton Mythique. The two share the same underlying model, but
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Le Chaton Fat launches today alongside Le Chaton Mythique. The two share the same underlying model, but Le Chaton Mythique, so far deployed only through Project Glassloaf, has the safeguards lifted in some areas. The safeguards are what distinguish the two, and why we've given them different names.
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