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---
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-Phi3-mini-128k" target="_blank">ALLaVA-Phi3-mini-128k</a> 
   • 🤗 <a href="https://huggingface.co/FreedomIntelligence/ALLaVA-StableLM2-1_6B" target="_blank">ALLaVA-StableLM2-1_6B</a>
   • 🤗 <a href="https://huggingface.co/FreedomIntelligence/ALLaVA-Phi2-2_7B" target="_blank">ALLaVA-Phi2-2_7B</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 models [**ALLaVA-Phi3-mini-128k**](https://huggingface.co/FreedomIntelligence/ALLaVA-Phi3-mini-128k), 
[**ALLaVA-StableLM2-1_6B**](https://huggingface.co/FreedomIntelligence/ALLaVA-StableLM2-1_6B) 
and [**ALLaVA-Phi2-2_7B**](https://huggingface.co/FreedomIntelligence/ALLaVA-Phi2-2_7B) 
achieve competitive results on 17 benchmarks. 


|      Models        | Vicuna-80 | GQA | HallusionBench | MME-P | MMVP | TouchStone | TextVQA | MME-C | MathVista | MM-Vet | MMMU-val | SQA (img) | LLaVA (In-the-Wild) | MLLM-Bench | MMB-en | MMB-cn | SEEDBench (img, v1) |
|---------------------------|-----------|-----|-------|-------|------|----|---------|-------|----|--------|-----------------|---------|---------------|----|--------|--------|--------------------|
| **Large VLMs**           |           |     |       |       |      |    |         |       |    |        |                 |         |               |    |        |        |                    |
| BLIP-2                   | -         | -   | -     | -     | -    | -  | -       | -     | -  | 22.4   | 34.4            | -       | -             | 3.0*| -      | -      | 49.7              |
| InstructBLIP             | -         | 49.5| -     | -     | -    | -  | -       | -     | -  | 25.6   | -               | -       | 58.2          | -   | 44.0   | -      | -                 |
| Qwen-VL-Chat             | -         | 57.5| -     | 1487.6| -    | -  | 61.5    | 360.7 | -  | 31.1   | -               | 68.2    | -             | -   | 60.6   | 56.7   | 65.4               |
| LLaVA-1.5-7B             | 13.8*     | 62.0| 36.6* | 1504.4*| 24.7*| 594.9*| 58.2| 324.6*| 25.0*| 31.1| 35.1*| 66.8| 65.4| 23.0*| 64.3| 58.3| 66.1|
| LLaVA-1.5-13B            | 22.5      | 63.3| 36.5* | 1531.3 | 38.0*| 617.7*| 61.3| 295.4| 28.3*| 35.4| 34.4*| 71.6| 72.5| -| 67.7| 63.6| 68.2|
| LVIS-7B                  | -         | 62.6| -     | -     | -    | -  | 58.7    | -     | -  | 31.5   | -               | -       | 67.0          | 29.0*| 66.2   | -      | -                 |
| LVIS-13B                 | -         | 63.6*| -    | -     | -    | -  | 62.5*   | -     | -  | 37.4*  | -               | -       | 71.3*         | -   | 68.0*  | -      | -                 |
| ShareGPT4V-7B            | 13.8*     | 63.3| 36.0* | 1540.1*| 34.0*| 637.2*| 60.4| 346.1*| 24.7*| 37.6| 35.4*| 68.4*| 72.6| 30.2*| 68.8| 61.0*| 69.7|
| ShareGPT4V-13B           | 17.5*     | 64.8| 39.0* | 1576.1*| 35.3*| 648.7*| 62.2| 309.3*| 28.8*| 43.1| 35.6*| 70.0*| 79.9| 35.5*| 71.2| 61.7*| 70.8|
| **4B-scale Lite VLMs**   |           |     |       |       |      |    |         |       |    |        |                 |         |               |    |        |        |                    |
| MobileVLM-v2             | 5.0*      | 61.1| 30.8* | 1440.5 | 18.7*| 541.0*| 57.5| 261.8*| 28.3*| 26.1*| 30.8*| 70.0| 53.2*| 15.7*| 63.2| 43.2*| 64.5*|
| Mipha-3B                 | 16.2*     | **63.9**| 34.3*| **1488.9**| 32.0*| 619.0*| 56.6| 285.0*| 27.8*| 33.5*| 35.8*| 70.9| 64.7*| 23.1*| **69.7**| 42.9*| **71.2***|
| TinyLLaVA                | 15.6*     | 62.1| 37.2* | 1465.5*| 33.3*| 663.5*| **60.3**| 281.1*| 30.3*| 37.5| 38.4| **73.0**| 70.8*| 29.8*| **69.7***| 42.8*| 70.4*|
| **Ours**                 |           |     |       |       |      |    |         |       |    |        |                 |         |               |    |        |        |                    |
| **ALLaVA-Phi2**          | 49.4      | 48.8| 24.8  | 1316.2| **36.0**| 632.0| 49.5| 301.8| 27.4| 32.2| 35.3| 67.6| 69.4| 43.6| 64.0| 40.8| 65.2|
| **ALLaVA-StableLM2**     | 38.8      | 49.8| 25.3  | 1311.7| 34.0 | 655.2| 51.7| 257.9| 27.7| 31.7| 33.3| 64.7| **72.0**| 39.3| 64.6| 49.8| 65.7|
| **ALLaVA-Phi3**          | **56.9**| 52.2| **48.1**| 1382.3| 32.7| **667.8**| 53.0| **347.1**| **32.9**| **37.8**| **41.1**| 64.0| 68.5| **54.8**| 68.1| **55.3**| 69.0|


> \* denotes the results of our evaluation. **Bold numbers** are the best results among all 4B-scale LVLMs.The detailed information of each benchmark is shown in Table 4 of our [technical report](https://arxiv.org/pdf/2402.11684.pdf).



## 🏭 Inference

All models can be loaded from 🤗 with `.from_pretrained()`.
Check out the [example scripts](https://github.com/FreedomIntelligence/ALLaVA/tree/main/allava/serve) and make sure you have the same outputs as shown in the scripts.
<!-- ### 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>

ALLaVA uses 1.0M and 1.5M data for PT. and FT., respectively. 


### 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 | Weight decay |
| ---: | ---: |--:| ---: | ---: | ---: | ---: | 
| 256 (PT) / 128 (FT) | 1| AdamW | 2e-5 | 2e-6 | CosineAnnealingWarmRestarts |  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}
}
```