xiuyul / Lloco-7b-qmsum

LLoCO: Learning Long Contexts Offline

Paper | Code

Lloco-7b-qmsum is the LoRA adaptor checkpoint finetuned from AutoCompressor-Llama-2-7b-6k and meta-llama/Llama-2-7b-hf using the LLoCO method in LLoCO: Learning Long Contexts Offline . It is instruction-tuned on the QMSum training set.

LLoCO enables LLMs to process long-context efficiently by learning contexts offline through context compression and in-domain parameter-efficient finetuning with LoRA. This approach extends the effective context window of a 4k token LLaMA2-7B model to handle up to 128k tokens, while using 30x fewer tokens and achieving up to 7.62x inference speed-up.

Released LoRA Checkpoint

Citation

If you find this project useful, please consider citing:

@article{tan2024lloco,
  title={LLoCO: Learning Long Contexts Offline},
  author={Tan, Sijun and Li, Xiuyu and Patil, Shishir and Wu, Ziyang and Zhang, Tianjun and Keutzer, Kurt and Gonzalez, Joseph E and Popa, Raluca Ada},
  journal={arXiv preprint arXiv:2404.07979},
  year={2024}
}

Evaluation

Check out LLoCO: Learning Long Contexts Offline for evaluation results on various long-context tasks such as long document question answering and summarization.