---
license: apache-2.0
language:
  - en
library_name: transformers
base_model: LiquidAI/LFM2.5-8B-A1B
pipeline_tag: text-generation
tags:
  - reap
  - pruning
  - moe
  - lfm2
  - liquid-ai
---

# LFM2.5-8B-A1B-REAP-50

## Model Description

This is a **50% expert-pruned** variant of [LiquidAI/LFM2.5-8B-A1B](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B), produced by **REAP** (Ranking and Eliminating Experts via Activation Patterns) pruning.

- **Base**: LFM2.5-8B-A1B — 32 experts per MoE layer, 8.47B params, 16.94 GB (bf16)
- **This model**: 16 experts per MoE layer, 4.59B params, 8.57 GB (bf16)
- **Compression**: 45.5% size reduction, 50% expert removal
- **No fine-tuning**: REAP produces an exact subset of original expert weights — gate/router weights and surviving expert weights are bit-for-bit identical to the base model.

## How REAP Works

REAP scores each expert's importance using activation patterns from a calibration set, then prunes the lowest-importance experts **per-layer independently**. This is not uniform pruning — different experts survive in different layers:

- Expert 27 survives in 73% of layers (most important)
- Expert 29 survives in 32% of layers (most pruned)

**Calibration**: 200 samples from `evol-codealpaca-v1`
**Tool**: [reap-cuda](https://github.com/egesabanci/reap-cuda)

## Loading

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "konic-labs/LFM2.5-8B-A1B-REAP-50",
    trust_remote_code=True,
    dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(
    "konic-labs/LFM2.5-8B-A1B-REAP-50", trust_remote_code=True
)

messages = [{"role": "user", "content": "What is 2+2?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, do_sample=False, temperature=None)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

Works with **vLLM** out of the box (native `Lfm2MoeForCausalLM` support).

## Benchmark Results

| Benchmark | Base (published) | This model (REAP-50) | Retention |
|-----------|:------:|:------:|:------:|
| MATH500 | 88.76% | **77.0%** | 86.7% |
| BFCLv3 (single-turn) | 64.79% | **59.07%** | 91.1% |

### MATH500 per-difficulty

| Level | Accuracy |
|-------|:--------:|
| L1 | 90.7% |
| L2 | 84.4% |
| L3 | 81.9% |
| L4 | 82.8% |
| L5 | 58.2% |

### BFCLv3 per-category

| Category | Accuracy |
|----------|:--------:|
| simple_python | 78.50% |
| multiple | 85.00% |
| parallel | 70.00% |
| parallel_multiple | 70.50% |
| irrelevance | 80.00% |
| live_simple | 60.47% |
| live_multiple | 54.32% |
| live_parallel | 31.25% |
| live_parallel_multiple | 54.17% |
| live_irrelevance | 75.90% |
| live_relevance | 68.75% |

## Throughput (vLLM, NVIDIA L4)

| Config | Tokens/sec |
|--------|:-----------:|
| Offline batch (500 prompts) | 1,280 |
| HTTP server (conc=256) | 2,282 |

## Intended Use

Research and deployment of compressed MoE models. This is the intermediate pruning stage — apply your own quantization (AWQ, GPTQ, etc.) on top, or use directly in bf16.

## Citation

```
@software{reap-cuda,
  title  = {REAP: Ranking and Eliminating Experts via Activation Patterns},
  author = {Konic Labs},
  url    = {https://github.com/egesabanci/reap-cuda}
}
```

## License

Apache-2.0 (inherited from base model).