Text Generation
MLX
Safetensors
English
mistral
reasoning
mlx-my-repo
conversational
Eval Results (legacy)
8-bit precision
Instructions to use kaijinlin7777/Mistral-Small-24B-Instruct-2501-reasoning-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use kaijinlin7777/Mistral-Small-24B-Instruct-2501-reasoning-mlx-8Bit 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("kaijinlin7777/Mistral-Small-24B-Instruct-2501-reasoning-mlx-8Bit") 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 kaijinlin7777/Mistral-Small-24B-Instruct-2501-reasoning-mlx-8Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "kaijinlin7777/Mistral-Small-24B-Instruct-2501-reasoning-mlx-8Bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "kaijinlin7777/Mistral-Small-24B-Instruct-2501-reasoning-mlx-8Bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kaijinlin7777/Mistral-Small-24B-Instruct-2501-reasoning-mlx-8Bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
- Xet hash:
- 2901add74e473a6266ee334cb3f40f812222db9a6abc712020ca2ec450b8fd62
- Size of remote file:
- 5.31 GB
- SHA256:
- d58098158fd2d3e845a1e4b7ecbd7de399eae45b89b07733a638754e0377d2a3
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