transformers safetensors llava_phi text-generation custom_code en dataset:FreedomIntelligence/ALLaVA-4V arxiv:2402.11684 license:apache-2.0 autotrain_compatible region:us
---
license: apache-2.0
datasets:
- FreedomIntelligence/ALLaVA-4V
language:
- en
pipeline_tag: text-generation
---


# ALLaVA: Harnessing  GPT4V-synthesized Data for A Lite Vision-Language Model



<p align="center">
⚡ALLaVA is a project that provides a large-scale GPT4V-synthesized  dataset for training LVLMs.⚡
</p>

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<p align="center">
   📃 <a href="https://arxiv.org/abs/2402.11684" target="_blank">Paper</a>  • 🌐 <a href="https://allava.freedomai.cn/#/" target="_blank">Demo</a>  • 👨🏻‍💻 <a href="https://github.com/FreedomIntelligence/ALLaVA" target="_blank">Github</a> 
</p>
<p align="center">
   🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ALLaVA-4V" target="_blank">ALLaVA-4V Dataset</a> 
</p>
   
<p align="center">
   🤗 <a href="https://huggingface.co/FreedomIntelligence/ALLaVA-3B-Longer" target="_blank">ALLaVA-3B-Longer</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/ALLaVA-3B" target="_blank">ALLaVA-3B</a>
</p>

<!-- <p align="center">
   📃 <a href="https://arxiv.org/abs/2402.11684" target="_blank">Paper</a>  • 🌐 <a href="https://allava.freedomai.cn/#/" target="_blank">Demo</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ALLaVA-4V" target="_blank">ALLaVA-4V Dataset</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/ALLaVA-3B-Longer" target="_blank">ALLaVA-3B-Longer</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/ALLaVA-3B" target="_blank">ALLaVA-3B</a>
    <br>  <a href="https://github.com/FreedomIntelligence/CMB/blob/main/README_zh.md">   中文</a> | <a href="https://github.com/FreedomIntelligence/CMB/blob/main/README.md"> English
</p> -->

## Benchmark Result

Our model [**ALLaVA-3B-Longer**](https://huggingface.co/FreedomIntelligence/ALLaVA-3B-Longer) and [**ALLaVA-3B**](https://huggingface.co/FreedomIntelligence/ALLaVA-3B) achieve competitive results on 12 benchmarks. Bold numbers denote the SOTA performance among 3B-scale models.

| Model | Backbone | Vicuna-80 | MMB | SEEDBench-v1 (img) | MM-Vet | MMMU (val) | MME | TextVQA | GQA | EMT (CIFAR10) | MLLM-Bench | TouchStone | LLaVA (In-the-Wild) |
|-------|----------|-----------|-----|-------------|--------|----------|-----|------|-----|---------|----|----|--------|
| Qwen-VL-Chat | Qwen-7B | - | 60.6 | 65.4 | - | 35.9 | 1487.5 | 61.5 | 57.5 | - | 6.2 | 711.6 | - |
| LLaVA-v1.5-7B | Vicuna-7B | - | 64.3 | - | 31.1 | - | 1510.7 | 58.2 | 62.0 | - | - | | 65.4 |
| LLaVA-v1.5-13B | Vicuna-13B | 22.50 | 67.7 | 68.2 | 35.4 | 36.4 | 1531.3 | 61.3 | 63.3 | 85.0 | 7.4 | 637.7 | 70.7 |
| ShareGPT4V-7B | Vicuna-7B | - | 68.8 | 69.7 | 37.6 | - | 1943.8 | 60.4 | 63.3 | - | - | - | 72.6 |
| TinyGPT-V | Phi2-2.7B | - | - | - | - | - | - | - | 33.6 | - | - | - | - |
| MobileVLM | MobileLLaMA-2.7B | - | 59.6 | - | - | - | 1288.9 | 47.5 | - | - | - | - | - |
| LLaVA-Phi | Phi2-2.7B | - | 59.8 | - | 28.9 | - | 1335.1 | 48.6 | - | - | - | - | - |
| **ALLaVA-3B** | Phi2-2.7B | 48.8 | 64.0 | 65.2 | 32.2 | **35.3** | **1623.2** | 49.5 | 48.8 | **90.2** | 6.7 | 632.0 | 69.4 |
| **ALLaVA-3B-Longer** | Phi2-2.7B | **52.5** | **64.6** | **65.6** | **35.5** | 33.2 | 1564.6 | **50.3** | **50.0** | 85.9 | **8.8** | **636.5** | **71.7** |

The detailed information of each benchmark is shown in Table 4 of our [technical report](https://arxiv.org/pdf/2402.11684.pdf).



## 🏭 Inference

### Load from 🤗 (Recommended) 
See the [example script](https://github.com/FreedomIntelligence/ALLaVA/blob/main/allava/serve/huggingface_inference.py).

### CLI
See [here](https://github.com/FreedomIntelligence/ALLaVA/tree/main?tab=readme-ov-file#cli) for CLI code snippet.



## 🏋️‍♂️ Training

### Data
<div align=center>
<img src="training_datasets_by_stage.jpg" width = "640" alt="training_datasets" align=center />
</div>

As shown in the table, ALLaVA-3B uses 1M and 1.5M data for PT. and FT., respectively. 
ALLaVA-3B-Longer trains one more epoch (i.e. 3M in total) for the FT. stage.

### Code
The training code is largely based on [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA). 
We wholeheartedly express our gratitude for their invaluable contributions to open-sourcing LVLMs.

### Cost
We train our models on 8*A800 GPUs.
[ALLaVA-3B-Longer](https://huggingface.co/FreedomIntelligence/ALLaVA-3B-Longer) takes 8.3h for PT and 21.3h for FT.
[ALLaVA-3B](https://huggingface.co/FreedomIntelligence/ALLaVA-3B) takes 8.3h for PT and 10.6h for FT.
These two models share the same PT procedure.


### Hyperparameters

| Global Batch Size| ZeRO Stage| Optimizer | Max LR| Min LR | Scheduler | Max length | Weight decay |
| ---: | ---: |--:| ---: | ---: | ---: | ---: | ---: |
| 256 (PT) / 128 (FT) | 1| AdamW | 2e-5 | 2e-6 | CosineAnnealingWarmRestarts | 2048 | 0 |

The LM backbone, projector are trainable, while the vision encoder is kept frozen. 
**The trainabilities of each module are the same for both stages.**


## 📚 ALLaVA-4V Data

The majority part of training data is [ALLaVA-4V](https://huggingface.co/datasets/FreedomIntelligence/ALLaVA-4V). See [here](https://github.com/FreedomIntelligence/ALLaVA/tree/main?tab=readme-ov-file#data-preparation) to prepare it for training. 


## 🙌 Contributors

- Project Leader: [Guiming Hardy Chen](https://g-h-chen.github.io/)

- Data: Shunian Chen, [Junying Chen](https://jymchen.github.io/), Xiangbo Wu

- Evaluation: [Ruifei Zhang](https://scholar.google.com/citations?user=W4zOhmEAAAAJ&hl=zh-CN)

- Deployment: Xiangbo Wu, Zhiyi Zhang

- Advising: [Zhihong Chen](https://zhjohnchan.github.io/), [Benyou Wang](https://wabyking.github.io/old.html)

- Others: Jianquan Li, [Xiang Wan](https://scholar.google.com/citations?user=e3_kWigAAAAJ&hl=zh-CN)





## 📝 Citation
If you find our data useful, please consider citing our work! We are FreedomIntelligence from [Shenzhen Research Institute of Big Data](http://sribd.cn/en) and [The Chinese University of Hong Kong, Shenzhen](https://sds.cuhk.edu.cn/en)
```
@article{chen2024allava,
  title={ALLaVA: Harnessing GPT4V-synthesized Data for A Lite Vision-Language Model},
  author={Chen, Guiming Hardy and Chen, Shunian and Zhang, Ruifei and Chen, Junying and Wu, Xiangbo and Zhang, Zhiyi and Chen, Zhihong and Li, Jianquan and Wan, Xiang and Wang, Benyou},
  journal={arXiv preprint arXiv:2402.11684},
  year={2024}
}
```
