Instructions to use shieldstackllc/MiniMax-M2.5-REAP-29-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shieldstackllc/MiniMax-M2.5-REAP-29-mlx-4bit 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-29-mlx-4bit")

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 shieldstackllc/MiniMax-M2.5-REAP-29-mlx-4bit 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-29-mlx-4bit"

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-29-mlx-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use shieldstackllc/MiniMax-M2.5-REAP-29-mlx-4bit with OpenClaw:

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-29-mlx-4bit"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "shieldstackllc/MiniMax-M2.5-REAP-29-mlx-4bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use shieldstackllc/MiniMax-M2.5-REAP-29-mlx-4bit 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-29-mlx-4bit"

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-29-mlx-4bit"
# 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-29-mlx-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use shieldstackllc/MiniMax-M2.5-REAP-29-mlx-4bit 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-29-mlx-4bit"

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-29-mlx-4bit

Run Hermes

hermes

Atomic Chat

MiniMax-M2.5-REAP-29-mlx-4bit / README.md

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	---
	language:
	- en
	license: mit
	pipeline_tag: text-generation
	tags:
	- mlx
	- mixture-of-experts
	- moe
	- pruning
	- reap
	- minimax
	- 4bit
	- quantized
	- apple-silicon
	library_name: mlx
	base_model: Akicou/MiniMax-M2-5-REAP-29
	---

	<p align="center">
	<a href="https://vmlx.net">
	<img src="vmlx-logo.png" alt="vMLX" width="120">
	</a>
	</p>

	# MiniMax-M2.5 REAP-29 — MLX 4-bit

	MLX 4-bit quantized version of [Akicou/MiniMax-M2-5-REAP-29](https://huggingface.co/Akicou/MiniMax-M2-5-REAP-29) for efficient local inference on Apple Silicon.

	- Quantization: 4-bit (group size 64, affine mode; router gates at 8-bit)
	- Architecture: MiniMax M2.5 MoE — 62 layers, 180 experts (REAP-pruned from 256), 8 active per token
	- Context: 196K tokens
	- Size: ~85 GB
	- Pruning: 29% of experts removed via [REAP](https://github.com/CerebrasResearch/reap) (Router Expert Activation Pruning)

	## Usage

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("shieldstackllc/MiniMax-M2.5-REAP-29-mlx-4bit")
	response = generate(model, tokenizer, prompt="Hello!", verbose=True)
	```

	Or with [vMLX](https://vmlx.net) for native macOS inference.

	## About

	MiniMax-M2.5 is a large Mixture-of-Experts language model by MiniMax AI. This variant was pruned to 29% fewer experts by [Akicou](https://huggingface.co/Akicou) using REAP (Router Expert Activation Pruning), reducing model size and memory footprint while maintaining strong performance. MLX quantization by [vMLX](https://vmlx.net).

	## Also Available

	- [MiniMax-M2.5-REAP-39 MLX 4-bit](https://huggingface.co/shieldstackllc/MiniMax-M2-5-REAP-39-mlx-4bit) (~73 GB) — 39% pruned variant
	- [MiniMax-M2.5-REAP-39 MLX 8-bit](https://huggingface.co/shieldstackllc/MiniMax-M2-5-REAP-39-mlx-8bit) (~138 GB) — 39% pruned variant

	## Made for vMLX

	This model was converted and optimized for [vMLX](https://vmlx.net) — 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](https://huggingface.co/MiniMaxAI/MiniMax-M2.5) by MiniMax AI
	- REAP pruning: [Akicou/MiniMax-M2-5-REAP-29](https://huggingface.co/Akicou/MiniMax-M2-5-REAP-29) by Akicou
	- MLX conversion: [vMLX](https://vmlx.net) — Run AI locally on Mac. No compromises.

	## Contact

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