Instructions to use catalystsec/MiniMax-M2-3bit-DWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use catalystsec/MiniMax-M2-3bit-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-M2-3bit-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-M2-3bit-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-M2-3bit-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-M2-3bit-DWQ"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use catalystsec/MiniMax-M2-3bit-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-M2-3bit-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-M2-3bit-DWQ

Run Hermes

hermes

MLX LM

How to use catalystsec/MiniMax-M2-3bit-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-M2-3bit-DWQ"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "catalystsec/MiniMax-M2-3bit-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-M2-3bit-DWQ",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

runs in LMstudio

by bobig - opened Nov 1, 2025

Discussion

bobig

Nov 1, 2025

•

edited Nov 1, 2025

No issues, use the latest Beta LMstudio 0.3.31

and latest runtime extension:
LM Studio MLX
v0.31.0

bobig

Nov 1, 2025

50 TPS on M4 max.
Seems pretty smart, testing.

DaniDubi

Nov 2, 2025

Do you know how this 3bit-DWQ differs compared to mlx-community/MiniMax-M2-3bit?
Based on file size and looking at config.json quantization bits and group size they are identical!

Thanks for any info

kernelpool

Catalyst Security org Nov 2, 2025

DWQ trains the quantization scales and biases to match the output of the full precision model. It generally preserves quality better than standard quantization which just rounds the weights. You can read more about DWQ here: https://github.com/ml-explore/mlx-lm/pull/129

DaniDubi

Nov 2, 2025

@kernelpool thank you for the info! Looks promising indeed.

However the few evals tables showed in the PR was done only on tiny models (Qwen 1.7B & Llama 3.2 1B).
I wonder if there is any new reliable evals on much larger models.

kernelpool

Catalyst Security org Nov 3, 2025

Yeah, running things like MMLU Pro is quite time consuming for these larger models, so I tend to just use perplexity testing to get a sense of the improvement (in addition to real worl testing).

kernelpool changed discussion status to closed Nov 5, 2025

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