Instructions to use pipenetwork/MiniMax-M3-MLX-3bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pipenetwork/MiniMax-M3-MLX-3bit 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-3bit")

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-3bit 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-3bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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-3bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use pipenetwork/MiniMax-M3-MLX-3bit 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-3bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "pipenetwork/MiniMax-M3-MLX-3bit"
# 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-3bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use pipenetwork/MiniMax-M3-MLX-3bit 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-3bit"

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-3bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use pipenetwork/MiniMax-M3-MLX-3bit 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-3bit"

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-3bit" \
  --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"

pipenetwork commited on Jun 13

Commit

2e901d4

verified ·

1 Parent(s): 8660ae2

Add quantization cross-links + collection

Browse files

Files changed (1) hide show

README.md +12 -0

README.md CHANGED Viewed

@@ -28,6 +28,18 @@ projector and multi-token-prediction heads are not included). The model is a
 3 layers dense), with per-head QK-norm, partial RoPE, Gemma-style RMSNorm and the
 SwiGLU-OAI activation.
 ## Attention / context note
 MiniMax Sparse Attention (MSA) is implemented here as **full causal attention**.

 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](https://huggingface.co/collections/pipenetwork/minimax-m3-mlx-6a2d7776d4e2a69aad841516).
+| Variant | Size | Notes |
+|---|---|---|
+| [8-bit](https://huggingface.co/pipenetwork/MiniMax-M3-MLX-8bit) | ~453 GB | near-lossless |
+| [6-bit](https://huggingface.co/pipenetwork/MiniMax-M3-MLX-6bit) | ~346 GB | high quality |
+| [4-bit](https://huggingface.co/pipenetwork/MiniMax-M3-MLX-4bit) | ~240 GB | balanced default |
+| **3-bit** (this repo) | ~186 GB | smallest |
+| [mixed-3_6bit](https://huggingface.co/pipenetwork/MiniMax-M3-MLX-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**.