docs/mlx_deploy_guide.md

MLX deployment guide

Run, serve, and fine-tune MiniMax-M2.1 locally on your Mac using the MLX framework. This guide gets you up and running quickly.

Requirements

• Apple Silicon Mac (M3 Ultra or later)
• At least 256GB of unified memory (RAM)

Installation

Install the mlx-lm package via pip:

pip install -U mlx-lm

CLI

Generate text directly from the terminal:

mlx_lm.generate \
  --model mlx-community/MiniMax-M2.1-4bit \
  --prompt "How tall is Mount Everest?"

Add --max-tokens 256 to control response length, or --temp 0.7 for creativity.

Python Script Example

Use mlx-lm in your own Python scripts:

from mlx_lm import load, generate

# Load the quantized model
model, tokenizer = load("mlx-community/MiniMax-M2.1-4bit")

prompt = "Hello, how are you?"

# Apply chat template if available (recommended for chat models)
if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )

# Generate response
response = generate(
    model,
    tokenizer,
    prompt=prompt,
    max_tokens=256,
    temp=0.7,
    verbose=True
)

print(response)

Tips

• Model variants: Check this MLX community collection on Hugging Face for MiniMax-M2.1-4bit, 6bit, 8bit, or bfloat16 versions.
• Fine-tuning: Use mlx-lm.lora for efficient parameter-efficient fine-tuning (PEFT).

Resources

• GitHub: https://github.com/ml-explore/mlx-lm
• Models: https://huggingface.co/mlx-community