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---
license: apache-2.0
datasets:
- allenai/qasper
metrics:
- f1
---
# LLoCO: Learning Long Contexts Offline
[**Paper**](https://arxiv.org/abs/2404.07979) | [**Code**](https://github.com/jeffreysijuntan/lloco)
Lloco-7b-qasper is the LoRA adaptor checkpoint finetuned from [AutoCompressor-Llama-2-7b-6k](https://huggingface.co/princeton-nlp/AutoCompressor-Llama-2-7b-6k/) and [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
using the **LLoCO** method in [LLoCO: Learning Long Contexts Offline](https://arxiv.org/abs/2404.07979). It is instruction-tuned on the Qasper 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
| Model | LoRA Rank | Dataset | Link |
|:----------------|-----------|-------------|--------------------------------------------------------|
| Lloco-7b-quality| 8 | QuALITY | [link](https://huggingface.co/xiuyul/Lloco-7b-quality/)|
| Lloco-7b-qasper | 8 | Qasper | [link](https://huggingface.co/xiuyul/Lloco-7b-qasper/) |
| Lloco-7b-qmsum | 8 | QMSum | [link](https://huggingface.co/xiuyul/Lloco-7b-qmsum/) |
| Lloco-7b-nqa | 8 | NarrativeQA | [link](https://huggingface.co/xiuyul/Lloco-7b-nqa/) |
| Lloco-7b-hqa | 8 | HotpotQA | [link](https://huggingface.co/xiuyul/Lloco-7b-hqa/) |
## 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](https://arxiv.org/abs/2404.07979) for evaluation results on various long-context tasks such as long document question answering and summarization. |