Add vMLX model card

README.md

@@ -0,0 +1,68 @@
+---
+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**