---
language:
- en
license: mit
pipeline_tag: text-generation
tags:
- mlx
- mixture-of-experts
- moe
- pruning
- reap
- minimax
- 8bit
- quantized
- apple-silicon
library_name: mlx
base_model: Akicou/MiniMax-M2-5-REAP-19
---

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

# MiniMax-M2.5 REAP-19 — MLX 8-bit

MLX 8-bit quantized version of [Akicou/MiniMax-M2-5-REAP-19](https://huggingface.co/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](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-19-mlx-8bit")
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 19% 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-19 MLX 4-bit](https://huggingface.co/shieldstackllc/MiniMax-M2-5-REAP-19-mlx-4bit) (~107 GB)
- [MiniMax-M2.5-REAP-39 MLX 8-bit](https://huggingface.co/shieldstackllc/MiniMax-M2-5-REAP-39-mlx-8bit) (~138 GB)
- [MiniMax-M2.5-REAP-39 MLX 4-bit](https://huggingface.co/shieldstackllc/MiniMax-M2-5-REAP-39-mlx-4bit) (~73 GB)
- [MiniMax-M2.5-REAP-29 MLX 4-bit](https://huggingface.co/shieldstackllc/MiniMax-M2-5-REAP-29-mlx-4bit)

## 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-19](https://huggingface.co/Akicou/MiniMax-M2-5-REAP-19) by Akicou
- **MLX conversion**: [vMLX](https://vmlx.net) — Run AI locally on Mac. No compromises.

## Contact

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