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---
license: mit
---
<div align="center">
<!-- <img src="title.png" alt="LoRA-Flow" width="200"> -->
<!-- ***LORA-Flow*** -->
<b><i style="font-size: 24px;">LORA-Flow</i></b>
LoRAs and fusion gates for our paper
<p align="center">
<a href="https://aclanthology.org/2024.acl-long.695/">Paper</a> •
<a href="https://github.com/pingbowen23/LoRA-Flow"> Github</a>
</p>
</div>
We released all of our checkpoints used in [LoRA-Flow](https://aclanthology.org/2024.acl-long.695.pdf) which has been accepted to ACL 2024 main conference.
# Summary
> In this repo, we release LoRA modules and the gate of 7B models trained in our paper in HuggingFace format.
# Introduction
LoRA-Flow provides an efficient way to fuse different LoRA modules. The following picture shows our proposed method, we use layer-wise fusion gates to facilitate dynamic LoRA fusion, which project input hidden states of each layer into fusion weights. LoRA-flow can be applied into [Llama-7b backbone](https://huggingface.co/meta-llama/Llama-2-7b) . For more details, please refer to our paper.
# Training Data
## Data used for LoRA modules
For the language LoRA modules: we use the 52K training samples from [Okapi](https://aclanthology.org/2023.emnlp-demo.28) for each language, respectively.
For the math LoRA module: we use [Metamath](https://arxiv.org/abs/2309.12284) that is comprised of 395K mathematical problems and the corresponding solutions in English.
For the code LoRA module: we use the Magicoder dataset [Magicoder](https://arxiv.org/abs/2312.02120), which consists of 186K code generation problems and the corresponding solutions in English.
## Data used for gates
We use gates to fuse different LoRA modules. We employ few-shot training and have released our training data. For more details, please refer to our GitHub.
# Results
We have released the results for LoRAs and LoRA-Flow
<table border="1" cellspacing="0" cellpadding="10">
<thead>
<tr>
<th rowspan="2">Method</th>
<th colspan="4">MGSM (Math)</th>
<th colspan="4">HumanEval (Code)</th>
</tr>
<tr>
<th style="text-align: right;">Zh</th>
<th style="text-align: right;">Ru</th>
<th style="text-align: right;">Es</th>
<th style="text-align: right;">Avg.</th>
<th style="text-align: right;">Zh</th>
<th style="text-align: right;">Ru</th>
<th style="text-align: right;">Es</th>
<th style="text-align: right;">Avg.</th>
</tr>
</thead>
<tbody>
<tr>
<td>Base Model</td>
<td style="text-align: right;">4.4</td>
<td style="text-align: right;">3.2</td>
<td style="text-align: right;">2.4</td>
<td style="text-align: right;">3.3</td>
<td style="text-align: right;">0.0</td>
<td style="text-align: right;">0.0</td>
<td style="text-align: right;">2.4</td>
<td style="text-align: right;">0.8</td>
</tr>
<tr>
<td rowspan="2">Single LoRA</td>
<td style="text-align: right;">5.2</td>
<td style="text-align: right;">3.6</td>
<td style="text-align: right;">3.6</td>
<td style="text-align: right;">4.1</td>
<td style="text-align: right;">12.2</td>
<td style="text-align: right;">14.0</td>
<td style="text-align: right;">10.4</td>
<td style="text-align: right;">12.2</td>
</tr>
<tr>
<td style="text-align: right;">26.8</td>
<td style="text-align: right;">32.8</td>
<td style="text-align: right;">41.2</td>
<td style="text-align: right;">33.6</td>
<td style="text-align: right;">18.3</td>
<td style="text-align: right;">23.2</td>
<td style="text-align: right;">21.9</td>
<td style="text-align: right;">21.1</td>
</tr>
<tr>
<td rowspan="3">LoRA Fusion</td>
<td style="text-align: right;">12.8</td>
<td style="text-align: right;">10.4</td>
<td style="text-align: right;">18.4</td>
<td style="text-align: right;">13.9</td>
<td style="text-align: right;">17.1</td>
<td style="text-align: right;">17.7</td>
<td style="text-align: right;">18.3</td>
<td style="text-align: right;">17.7</td>
</tr>
<tr>
<td style="text-align: right;">20.8</td>
<td style="text-align: right;">28.4</td>
<td style="text-align: right;">36.8</td>
<td style="text-align: right;">28.7</td>
<td style="text-align: right;">19.5</td>
<td style="text-align: right;">21.3</td>
<td style="text-align: right;">20.1</td>
<td style="text-align: right;">20.3</td>
</tr>
<tr>
<td style="text-align: right;"><strong>33.2</strong></td>
<td style="text-align: right;"><strong>37.6</strong></td>
<td style="text-align: right;"><strong>42.0</strong></td>
<td style="text-align: right;"><strong>37.6</strong></td>
<td style="text-align: right;"><strong>20.7</strong></td>
<td style="text-align: right;"><strong>23.8</strong></td>
<td style="text-align: right;"><strong>23.2</strong></td>
<td style="text-align: right;"><strong>22.6</strong></td>
</tr>
</tbody>
</table>
# Citation
```bibtex
@inproceedings{wang-etal-2024-lora-flow,
title = "LoRA-Flow: Dynamic LoRA Fusion for Large Language Models in Generative Tasks",
author = "Wang, Hanqing and
Ping, Bowen and
Wang, Shuo and
Han, Xu and
Chen, Yun and
Liu, Zhiyuan and
Sun, Maosong",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.695",
doi = "10.18653/v1/2024.acl-long.695",
pages = "12871--12882"
}
```
<!-- [LoRA-Flow: Dynamic LoRA Fusion for Large Language Models in Generative Tasks](https://aclanthology.org/2024.acl-long.695) --> |