Instructions to use exdysa/MiniMax-M2.7-REAP-139B-A10B-MLX-5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use exdysa/MiniMax-M2.7-REAP-139B-A10B-MLX-5bit 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("exdysa/MiniMax-M2.7-REAP-139B-A10B-MLX-5bit")

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 exdysa/MiniMax-M2.7-REAP-139B-A10B-MLX-5bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "exdysa/MiniMax-M2.7-REAP-139B-A10B-MLX-5bit"

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": "exdysa/MiniMax-M2.7-REAP-139B-A10B-MLX-5bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use exdysa/MiniMax-M2.7-REAP-139B-A10B-MLX-5bit 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 "exdysa/MiniMax-M2.7-REAP-139B-A10B-MLX-5bit"

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 exdysa/MiniMax-M2.7-REAP-139B-A10B-MLX-5bit

Run Hermes

hermes

MLX LM

How to use exdysa/MiniMax-M2.7-REAP-139B-A10B-MLX-5bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "exdysa/MiniMax-M2.7-REAP-139B-A10B-MLX-5bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "exdysa/MiniMax-M2.7-REAP-139B-A10B-MLX-5bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "exdysa/MiniMax-M2.7-REAP-139B-A10B-MLX-5bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Original Model Link : dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B

name: MiniMax-M2.7-REAP-139B-A10B-5bit
base_model: MiniMaxAI/MiniMax-M2.7
license: other
license_name: modified-mit
license_link: https://huggingface.co/MiniMaxAI/MiniMax-M2.7/blob/main/LICENSE
pipeline_tag: text-generation
tasks: text-generation
language: en
library_name: mlx
size: 95GB
tags:
- Cerebras
- MiniMaxAI
- M2.7
- REAP
- MLX
- static quantization
- 4-bit
- moe
- pruning
- text-generation
- mlx

Description

This is a 230 billion parameter MiniMax M2.7 model with 40% of its experts pruned with REAP (Router-weighted Expert Activation Pruning), then converted to MLX with mlx_lm.

Conversion sequence using source version of mlx_lm@0.31.3 :

mlx_lm.convert --hf-path dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B --mlx-path ~/Downloads/MiniMax-M2.7-REAP-139B-A10B-MLX-5bit -q --q-bits 5

Downloads last month: 212

Safetensors

Model size

139B params

Tensor type

BF16

U32

MLX

Hardware compatibility

5-bit

Model tree for exdysa/MiniMax-M2.7-REAP-139B-A10B-MLX-5bit

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