Text Generation
MLX
Safetensors
English
mistral
codestral
code
apple-silicon
FIM
Fill-in-the-Middle
code-generation
4bit
conversational
Eval Results (legacy)
4-bit precision
Instructions to use CyberYui/Codestral-22B-Yui-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use CyberYui/Codestral-22B-Yui-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("CyberYui/Codestral-22B-Yui-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 CyberYui/Codestral-22B-Yui-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 "CyberYui/Codestral-22B-Yui-MLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "CyberYui/Codestral-22B-Yui-MLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CyberYui/Codestral-22B-Yui-MLX", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
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Verified signature# π¬π§ English | π¨π³ δΈζ > π Scroll down for **Chinese version** / εδΈζ»ε¨ζ₯η**δΈζηζ¬** --- ## π¬π§ English # Codestral-22B-Yui-MLX This model is **CyberYui's custom-converted MLX format port ** of Mistral AI's official [`mistralai/Codestral-22B-v0.1`](https://huggingface.co/mistralai/Codestral-22B-v0.1) model. No modifications, alterations, or fine-tuning of any kind were applied to the original model's weights, architecture, or parameters; this is strictly a format conversion for MLX, optimized exclusively for Apple Silicon (M1/M2/M3/M4) chips. ### π Model Details - **Base Model**: [`mistralai/Codestral-22B-v0.1`](https://huggingface.co/mistralai/Codestral-22B-v0.1) - **Conversion Tool**: `mlx-lm 0.29.1` - **Quantization**: 4-bit (β12.5GB total size) - **Framework**: MLX (native Apple GPU acceleration) - **Use Cases**: Code completion, code generation, programming assistance, FIM (Fill-In-the-Middle) ### π How to Use #### 1. Command Line (mlx-lm) First, install the required package:β¦d7d6ea4 -
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