Image-Text-to-Text
MLX
Safetensors
minimax_m3_vl
multimodal
Mixture of Experts
agent
coding
video
conversational
custom_code
4-bit precision
Instructions to use mlx-community/MiniMax-M3-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/MiniMax-M3-4bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("mlx-community/MiniMax-M3-4bit") config = load_config("mlx-community/MiniMax-M3-4bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use mlx-community/MiniMax-M3-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mlx-community/MiniMax-M3-4bit"
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": "mlx-community/MiniMax-M3-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mlx-community/MiniMax-M3-4bit 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 "mlx-community/MiniMax-M3-4bit"
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 mlx-community/MiniMax-M3-4bit
Run Hermes
hermes
Loading + batched generation on mlx_vlm (3 fixes)
#1
by RockTalk - opened
Stock mlx_vlm (incl. the in-flight MiniMax-M3 support PR) can't serve this 4-bit quant out of the box:
- load fails with
Received 855 parameters not in model(the MoE experts ship pre-stacked asblock_sparse_moe.switch_mlp.*+ separateshared_experts.*, which the sanitizer didn't fuse), and - once loaded, concurrent / best-of-N requests hit
MiniMaxM3KVCache does not yet support batching.
Three small fixes resolve both (load + batching). Submitted upstream here: https://github.com/ivanfioravanti/mlx-vlm/pull/2
Verified on an M3 Ultra (512GB): loads, generates ~23.4 tok/s, and a best-of-8 + unit-test coding bake-off scores 6/6 with batching on (CONC=4), 0 errors. Posting so others running this on Apple Silicon can find the fix.
— Rocktalk Holdings
Please try again now, I re uploaded it and it should be fixed.