LightTransfer

 

LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptation

 

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Introduction

LightTransfer is a lightweight transformation framework for enhancing the efficiency of large transformer models, such as LLaMA and QwQ, in long-context understanding and long CoT generation. By identifying lazy layers—those primarily attending to initial or recent tokens—LightTransfer replaces their full attention with streaming attention, significantly reducing memory overhead.

• Improved efficiency with minimal performance loss:
  LightTransfer achieves up to 2.17× higher throughput while maintaining strong performance (<1.5% drop on LongBench).
• Flexible adaptation for long-context tasks:
  Works without retraining for long-context understanding and requires only minimal fine-tuning for advanced long CoT generation, such as mathematical reasoning in QwQ-STILL, achieving 53.3% on AIME24.

For more details, visit our project page.

News

[2025.03.16] We release the checkpoint of QwQ-32B-LightTransfer. See model card for details.

LightTranfer-Train

We release the checkpoint of QwQ-LightTransfer, which is a 32B-parameter model built on Qwen/Qwen2.5-32B-Instruct and fine-tuned via SFT on RUC-AIBOX/long_form_thought_data_5k.

• By replacing 50% of the model’s full attention layers with streaming attention,specifically layers [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 33, 35, 37, 38, 43, 51], it substantially reduces memory costs.
• QwQ-LightTransfer scores 53.3% on the advanced math benchmark AIME24, demonstrating its strong o1-like long reasoning capabilities.

Performance Evaluation

We have evaluated QwQ-LightTransfer on several long reasoning generation benchmarks. Some of the evaluation results are shown in the table below.

Method	Math-OAI	AIME24	AIME25	GSM8K
o1-preview	85.5	44.6	-	-
QwQ-STILL	90.2	46.7	33.3	95.6
LongGen	78.2	16.7	-	95.4
LightTransfer	90.7	53.3	40.0	95.5

Usages

Import from Transformers

To load the QwQ-LightTransfer model using Transformers, use the following code:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = 'QwQ-32B-LightTransfer'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name,torch_dtype=torch.bfloat16,trust_remote_code=True,device_map='auto')

text = "Hi, I'm QwQ-32B-LightTransfer."
inputs = tokenizer(text, return_tensors='pt').to(model.device)
with torch.no_grad():
    outputs = model.generate(inputs['input_ids'],max_gen_len=32000)
print(tokenizer.decode(outputs[0]))

Evaluation scripts

License

Code and model weights are licensed under Apache-2.0.

Citation

@misc{zhang2025lighttransferlongcontextllmsecretly,
      title={LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptation}, 
      author={Xuan Zhang and Fengzhuo Zhang and Cunxiao Du and Chao Du and Tianyu Pang and Wei Gao and Min Lin},
      year={2025},
      eprint={2410.13846},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.13846}, 
}