Instructions to use shieldstackllc/MiniMax-M2-5-REAP-19-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shieldstackllc/MiniMax-M2-5-REAP-19-mlx-8bit 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("shieldstackllc/MiniMax-M2-5-REAP-19-mlx-8bit")

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 shieldstackllc/MiniMax-M2-5-REAP-19-mlx-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "shieldstackllc/MiniMax-M2-5-REAP-19-mlx-8bit"

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": "shieldstackllc/MiniMax-M2-5-REAP-19-mlx-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use shieldstackllc/MiniMax-M2-5-REAP-19-mlx-8bit 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 "shieldstackllc/MiniMax-M2-5-REAP-19-mlx-8bit"

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 shieldstackllc/MiniMax-M2-5-REAP-19-mlx-8bit

Run Hermes

hermes

MLX LM

How to use shieldstackllc/MiniMax-M2-5-REAP-19-mlx-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "shieldstackllc/MiniMax-M2-5-REAP-19-mlx-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "shieldstackllc/MiniMax-M2-5-REAP-19-mlx-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "shieldstackllc/MiniMax-M2-5-REAP-19-mlx-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

MiniMax-M2.5 REAP-19 — MLX 8-bit

MLX 8-bit quantized version of Akicou/MiniMax-M2-5-REAP-19 for efficient local inference on Apple Silicon.

Quantization: 8-bit (9.0 bits per weight, group size 64, affine mode)
Architecture: MiniMax M2.5 MoE — 62 layers, 205 experts (REAP-pruned from 256), 8 active per token
Context: 196K tokens
Size: ~193 GB
Pruning: 19% of experts removed via REAP (Router Expert Activation Pruning)

Usage

from mlx_lm import load, generate

model, tokenizer = load("shieldstackllc/MiniMax-M2-5-REAP-19-mlx-8bit")
response = generate(model, tokenizer, prompt="Hello!", verbose=True)

Or with vMLX for native macOS inference.

About

MiniMax-M2.5 is a large Mixture-of-Experts language model by MiniMax AI. This variant was pruned to 19% fewer experts by Akicou using REAP (Router Expert Activation Pruning), reducing model size and memory footprint while maintaining strong performance. MLX quantization by vMLX.

Also Available

MiniMax-M2.5-REAP-19 MLX 4-bit (~107 GB)
MiniMax-M2.5-REAP-39 MLX 8-bit (~138 GB)
MiniMax-M2.5-REAP-39 MLX 4-bit (~73 GB)
MiniMax-M2.5-REAP-29 MLX 4-bit

Made for vMLX

This model was converted and optimized for vMLX — a free, open source macOS native MLX inference engine for Apple Silicon. Download vMLX to run this model locally with zero configuration.

Credits

Base model: MiniMaxAI/MiniMax-M2.5 by MiniMax AI
REAP pruning: Akicou/MiniMax-M2-5-REAP-19 by Akicou
MLX conversion: vMLX — Run AI locally on Mac. No compromises.

Contact

For questions, issues, or collaboration: admin@vmlx.net

Downloads last month: 13

Safetensors

Model size

184B params

Tensor type

F32

U32

MLX

Hardware compatibility

8-bit

Model tree for shieldstackllc/MiniMax-M2-5-REAP-19-mlx-8bit

Base model

Akicou/MiniMax-M2-5-REAP-19

Quantized

(2)

this model