README.md

---
language:
- en
tags:
- mixture-of-experts
- moe
- pruning
- compression
- minimax
- reap
- efficient-inference
license: mit
library_name: transformers
base_model: MiniMaxAI/MiniMax-M2.5
pipeline_tag: text-generation
---

# MiniMax-M2.5 REAP-39 (39% Pruned)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Base Model](https://img.shields.io/badge/Base-MiniMax--M2.5-blue)](https://huggingface.co/MiniMaxAI/MiniMax-M2.5)
[![Pruning Method](https://img.shields.io/badge/Method-REAP-green)](https://github.com/CerebrasResearch/reap)

## Support This Work

Pruning large MoE models requires substantial GPU resources (multi-H100 clusters). If you find these models useful, consider [buying me a coffee](https://www.buymeacoffee.com/Akicou) to help offset rental costs and enable further releases. Your support makes this work possible!

## Overview

This repository contains a **REAP-pruned** variant of the **MiniMax-M2.5** Mixture-of-Experts (MoE) language model with **39%** of experts removed while maintaining strong performance.

**REAP** (Router Expert Activation Pruning) is a structured pruning technique that identifies and removes under-utilized experts based on activation patterns. This achieves:
- Reduced model size and memory footprint
- Faster inference and lower cost
- Maintained active parameters per token
- Full compatibility with HuggingFace Transformers

## REAP Variant Selection

Choose the variant that best fits your deployment constraints:

| Model | Pruned | Kept | Size Reduction | Performance Trade-off |
|-------|--------|------|----------------|----------------------|
| **REAP-10** | 10% | 90% | Small | Minimal |
| **REAP-20** | 20% | 80% | Moderate | Small |
| **REAP-30** | 30% | 70% | Significant | Moderate |
| **REAP-40** | 40% | 60% | Large | Noticeable |
| **REAP-50** | 50% | 50% | Very Large | Significant |

**Repository Links:**
- [`Akicou/MiniMax-M2.5-REAP-19`](https://huggingface.co/Akicou/MiniMax-M2.5-REAP-19)
- [`Akicou/MiniMax-M2.5-REAP-29`](https://huggingface.co/Akicou/MiniMax-M2.5-REAP-29)
- [`Akicou/MiniMax-M2.5-REAP-39`](https://huggingface.co/Akicou/MiniMax-M2.5-REAP-39)
- [`Akicou/MiniMax-M2.5-REAP-50`](https://huggingface.co/Akicou/MiniMax-M2.5-REAP-50)

## Quick Start

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Akicou/MiniMax-M2.5-REAP-39"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True
)

prompt = "Explain quantum entanglement in simple terms:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Memory-Efficient Loading

For systems with limited GPU memory:

```python
# 8-bit quantization
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    load_in_8bit=True,
    trust_remote_code=True
)

# 4-bit quantization
from transformers import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16
)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    quantization_config=quantization_config,
    trust_remote_code=True
)
```

## Quantized GGUF Versions

Quantized GGUF variants optimized for `llama.cpp`, `Ollama`, and similar backends are in preparation in collaboration with **mradermacher**. Planned formats include Q4_K_M, Q5_K_M, Q6_K, and Q8_0.

## 🔬 Pruning Methodology

### REAP Framework

Pruning was performed using the [REAP framework](https://github.com/CerebrasResearch/reap) (implementation: [Akicou/reap](https://github.com/Akicou/reap)) with the following configuration:

**Calibration Settings:**
- **Dataset:** Mixed-domain calibration corpus (150 samples per category)
- **Distance Metric:** Cosine similarity
- **Loading Precision:** 4-bit for memory efficiency during pruning
- **Selection Strategy:** Router activation frequency analysis

**Process:**
1. Collect expert activation statistics across calibration dataset
2. Compute similarity scores between experts
3. Identify and rank experts by utilization
4. Prune lowest-activated experts while maintaining coverage
5. Validate structural integrity and export pruned model

For full pruning commands, hyperparameters, and reproducibility details, see the [Akicou/reap repository](https://github.com/Akicou/reap).

## ⚖️ Performance Characteristics

**What Changes:**
- ✅ Reduced model size (fewer total experts)
- ✅ Faster inference (less expert routing overhead)
- ✅ Lower memory requirements
- ⚠️ Slight reduction in capability on edge cases

**What Stays the Same:**
- ✅ Active parameters per token (same compute per inference)
- ✅ Model architecture and API compatibility
- ✅ Tokenizer and input/output formats

**Trade-offs:** These models exchange a small amount of capability for significantly improved efficiency. Higher pruning rates (39 < 30%) may show more noticeable quality differences on complex or specialized tasks.

**Note:** Formal benchmarks are not provided due to resource constraints. Community evaluation contributions are welcome!

## 🛠️ Use Cases

**Ideal for:**
- 🏠 Running large language models on consumer GPUs
- 💻 Local development and testing
- 🌐 Edge deployment and on-device inference
- 💰 Cost-sensitive production environments
- 🔬 Research on efficient model architectures

**Consider the full model if:**
- You have abundant GPU resources
- Maximum quality is critical
- Working on highly specialized domains

## 📚 Citation

If you use these pruned models in your research or applications, please cite both the original REAP paper and the base model:

### REAP Citation

```bibtex
@article{lasby2025reap,
  title={REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
  author={Lasby, Mike and Lazarevich, Ivan and Sinnadurai, Nish and Lie, Sean and Ioannou, Yani and Thangarasa, Vithursan},
  journal={arXiv preprint arXiv:2510.13999},
  year={2025}
}
```

### Base Model Citation

```bibtex
@misc{minimax2025m25,
  title={MiniMax-M2.5: A State-of-the-Art Mixture-of-Experts Language Model},
  author={MiniMaxAI},
  year={2025},
  howpublished={\url{https://huggingface.co/MiniMaxAI/MiniMax-M2.5}}
}
```

## 🙏 Acknowledgments

- **Original Model:** [MiniMaxAI](https://huggingface.co/MiniMaxAI) for developing MiniMax-M2.5
- **REAP Framework:** [Cerebras Research](https://github.com/CerebrasResearch/reap) for the pruning methodology
- **Community:** HuggingFace and the open-source AI community

## 💖 Support This Work

Pruning large MoE models requires substantial computational resources (multi-GPU H100 clusters). If you find these models useful:

- ☕ [Buy me a coffee](https://www.buymeacoffee.com/Akicou) to help offset GPU rental costs
- ⭐ Star the [GitHub repository](https://github.com/Akicou/reap)
- 📢 Share with others who might benefit
- 🐛 Report issues and contribute improvements

Your support enables continued development and release of efficient model variants!

## 📞 Contact & Feedback

- **Issues & Requests:** Open an issue on [GitHub](https://github.com/Akicou/reap/issues)
- **Discussions:** Use the HuggingFace Community tab above
- **Custom Pruning:** Reach out for specific pruning ratios or other MoE models

Feedback, bug reports, and collaboration inquiries are always welcome!

## 📄 License

This model inherits the MIT license from the original MiniMax-M2.5 model. See [LICENSE](LICENSE) for details.

---

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