Instructions to use JANGQ-AI/MiniMax-M2.7-JANGTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JANGQ-AI/MiniMax-M2.7-JANGTQ 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("JANGQ-AI/MiniMax-M2.7-JANGTQ")

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 JANGQ-AI/MiniMax-M2.7-JANGTQ with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "JANGQ-AI/MiniMax-M2.7-JANGTQ"

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": "JANGQ-AI/MiniMax-M2.7-JANGTQ"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use JANGQ-AI/MiniMax-M2.7-JANGTQ 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 "JANGQ-AI/MiniMax-M2.7-JANGTQ"

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 JANGQ-AI/MiniMax-M2.7-JANGTQ

Run Hermes

hermes

MLX LM

How to use JANGQ-AI/MiniMax-M2.7-JANGTQ with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "JANGQ-AI/MiniMax-M2.7-JANGTQ"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "JANGQ-AI/MiniMax-M2.7-JANGTQ"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "JANGQ-AI/MiniMax-M2.7-JANGTQ",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

64gb mac version?

by performanceoptician - opened Apr 15

Discussion

performanceoptician

Apr 15

Any chances to make even smaller version capable running on ?

DaniDubi

Apr 17

@performanceoptician I'd definitely not recommended it.
From my experience most of MLX-quants of 3bit and lower (<=100GB) are broken and inconsistent, poor quality.

sainez

Apr 17

@DaniDubi are you talking about the naive 3bit quant or JANG quants? There is a whole webpage on how JANG can achieve extreme compression: https://jangq.ai

DaniDubi

Apr 18

@sainez about both, while I agree based on my limit testing that JANG quants of a similar size range are indeed better, still the quality of the quantized versions MiniMax M2.x models in general is not good. There are many reports that due to it's architecture it is quantized poorly, as opposed to Qwen-3.5 models that are much more resistant to low-quantization.

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