Instructions to use catalystsec/MiniMax-M2-4bit-DWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use catalystsec/MiniMax-M2-4bit-DWQ 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("catalystsec/MiniMax-M2-4bit-DWQ")

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
LM Studio

Pi new

How to use catalystsec/MiniMax-M2-4bit-DWQ with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "catalystsec/MiniMax-M2-4bit-DWQ"

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": "catalystsec/MiniMax-M2-4bit-DWQ"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use catalystsec/MiniMax-M2-4bit-DWQ 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 "catalystsec/MiniMax-M2-4bit-DWQ"

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 catalystsec/MiniMax-M2-4bit-DWQ

Run Hermes

hermes

MLX LM

How to use catalystsec/MiniMax-M2-4bit-DWQ with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "catalystsec/MiniMax-M2-4bit-DWQ"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "catalystsec/MiniMax-M2-4bit-DWQ"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "catalystsec/MiniMax-M2-4bit-DWQ",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

kernelpool commited on Nov 7, 2025

Commit

d185a17

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@@ -11,6 +11,18 @@ tags:
 This model was quantized to 4-bit using DWQ with mlx-lm version **0.28.4**.
 ## Use with mlx
 ```bash

 This model was quantized to 4-bit using DWQ with mlx-lm version **0.28.4**.
+| Parameter                 | Value                          |
+|---------------------------|--------------------------------|
+| DWQ learning rate         | 3e-7                           |
+| Batch size                | 1                              |
+| Dataset                   | `allenai/tulu-3-sft-mixture`   |
+| Initial validation loss   | 0.069                          |
+| Final validation loss     | 0.047                          |
+| Relative KL reduction     | ≈32 %                          |
+| Tokens processed          | ≈1.09 M                        |
+<img src="minimax4_3e-7.png" width="600" alt="Training loss curve">
 ## Use with mlx
 ```bash

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