konic-labs/LFM2.5-8B-A1B-REAP-50

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

Model Description

This is a 50% expert-pruned variant of 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

Loading

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

MATH500 per-difficulty

BFCLv3 per-category

Throughput (vLLM, NVIDIA L4)

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).