Instructions to use pipenetwork/MiniMax-M3-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pipenetwork/MiniMax-M3-MLX-4bit 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("pipenetwork/MiniMax-M3-MLX-4bit")

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

How to use pipenetwork/MiniMax-M3-MLX-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 "pipenetwork/MiniMax-M3-MLX-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": "pipenetwork/MiniMax-M3-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pipenetwork/MiniMax-M3-MLX-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 "pipenetwork/MiniMax-M3-MLX-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 pipenetwork/MiniMax-M3-MLX-4bit

Run Hermes

hermes

OpenClaw new

How to use pipenetwork/MiniMax-M3-MLX-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "pipenetwork/MiniMax-M3-MLX-4bit"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "pipenetwork/MiniMax-M3-MLX-4bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use pipenetwork/MiniMax-M3-MLX-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "pipenetwork/MiniMax-M3-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "pipenetwork/MiniMax-M3-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "pipenetwork/MiniMax-M3-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

MiniMax-M3-MLX-4bit / README.md

pipenetwork

Add quantization cross-links + collection

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metadata

license: other
license_name: minimax-community
license_link: LICENSE
base_model: MiniMaxAI/MiniMax-M3
base_model_relation: quantized
pipeline_tag: text-generation
library_name: mlx
tags:
  - mlx
  - moe
  - minimax
  - minimax-m3
  - text-generation

MiniMax-M3-MLX-4bit

Built with MiniMax M3.

This is an MLX (Apple Silicon) conversion of MiniMaxAI/MiniMax-M3, quantized to 4-bit (balanced default).

It is a text-only extraction of the M3 backbone (the vision tower, multimodal projector and multi-token-prediction heads are not included). The model is a ~427B-parameter Mixture-of-Experts (128 experts, top-4, + 1 shared expert; first 3 layers dense), with per-head QK-norm, partial RoPE, Gemma-style RMSNorm and the SwiGLU-OAI activation.

Quantizations

Part of the MiniMax-M3 MLX collection.

Variant	Size	Notes
8-bit	~453 GB	near-lossless
6-bit	~346 GB	high quality
4-bit (this repo)	~240 GB	balanced default
3-bit	~186 GB	smallest
mixed-3_6bit	~191 GB	experts@3-bit, attn/embeds/router@6-8-bit · best quality-per-GB

Attention / context note

MiniMax Sparse Attention (MSA) is implemented here as full causal attention. This is numerically exact for sequences up to 2048 tokens (MSA selects every key block at that length) and is the dense, un-approximated attention that MSA approximates beyond it — so quality is preserved, at the cost of MSA's long-context speed/memory savings.

Use with mlx-lm

pip install mlx-lm

This build requires the minimax_m3 model class (mlx_lm/models/minimax_m3.py, included in this repo — copy it into your mlx_lm/models/ directory).

from mlx_lm import load, generate

model, tokenizer = load("pipenetwork/MiniMax-M3-MLX-4bit")
prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Explain Mixture-of-Experts in one paragraph."}],
    add_generation_prompt=True,
)
print(generate(model, tokenizer, prompt=prompt, max_tokens=256, verbose=True))

License

Released under the MiniMax Community License (see LICENSE). Use is non-commercial by default; commercial use requires displaying "Built with MiniMax M3" and may require prior authorization from MiniMax — see the license text for details.

Provenance

Converted from the BF16 checkpoint with mlx-lm quantization. Quantization config: {"group_size": 64, "bits": 4, "mode": "affine", "model.layers.3.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.4.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.5.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.6.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.7.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.8.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.9.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.10.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.11.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.12.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.13.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.14.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.15.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.16.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.17.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.18.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.19.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.20.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.21.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.22.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.23.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.24.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.25.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.26.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.27.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.28.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.29.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.30.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.31.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.32.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.33.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.34.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.35.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.36.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.37.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.38.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.39.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.40.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.41.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.42.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.43.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.44.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.45.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.46.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.47.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.48.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.49.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.50.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.51.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.52.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.53.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.54.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.55.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.56.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.57.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.58.block_sparse_moe.gate": {"group_size": 64, "bits": 8}, "model.layers.59.block_sparse_moe.gate": {"group_size": 64, "bits": 8}}.