Instructions to use konic-labs/LFM2.5-8B-A1B-REAP-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use konic-labs/LFM2.5-8B-A1B-REAP-50 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="konic-labs/LFM2.5-8B-A1B-REAP-50")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("konic-labs/LFM2.5-8B-A1B-REAP-50")
model = AutoModelForCausalLM.from_pretrained("konic-labs/LFM2.5-8B-A1B-REAP-50", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use konic-labs/LFM2.5-8B-A1B-REAP-50 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "konic-labs/LFM2.5-8B-A1B-REAP-50"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "konic-labs/LFM2.5-8B-A1B-REAP-50",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/konic-labs/LFM2.5-8B-A1B-REAP-50

SGLang

How to use konic-labs/LFM2.5-8B-A1B-REAP-50 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "konic-labs/LFM2.5-8B-A1B-REAP-50" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "konic-labs/LFM2.5-8B-A1B-REAP-50",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "konic-labs/LFM2.5-8B-A1B-REAP-50" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "konic-labs/LFM2.5-8B-A1B-REAP-50",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use konic-labs/LFM2.5-8B-A1B-REAP-50 with Docker Model Runner:
```
docker model run hf.co/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

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).

Downloads last month: 65

Safetensors

Model size

5B params

Tensor type

F32

BF16

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

Base model

LiquidAI/LFM2.5-8B-A1B-Base

Finetuned

LiquidAI/LFM2.5-8B-A1B

Finetuned

(37)

this model