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"}
     ]
   }'

Vmlx app is on fire, thanks dev for creating this quant

by Narutoouz - opened Apr 14

Discussion

Narutoouz

Apr 14

•

edited Apr 14

Guys, best innovation is happening around apple silicon. Check out Mlx studio or Vmlx app. It is really nice

Omlx app is also good but vmlx has just upped its game like crazy with this quant
as a owner of m4 max macbook pro, I am delighted with this development happening around apple silicon and local AI. This is a deepseek moment for Local AI.
guys, download these apps and share your feedback for each of the projects in their github. Their devs are super talented ,receptive and ships changes very fast.
Thanks minimax team for democratizing AI for many apple silicon folks. Chinese companies are the real openAI. Use their API for local coding if you can't self host them. Support their development and current strategy.
nvidia, it is time to rethink your cloud AI strategy, future of AI is more local and less cloud. Cloud is for very powerful models and small models inference for public who can't access these high end machines.

mratsim

Apr 14

best innovation is happening around apple silicon.

Codebook + Hadamard transforms have been used in Exllama v3 on Nvidia GPU for over a year https://github.com/turboderp-org/exllamav3/blob/master/doc/exl3.md

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