Hugging Face's logo

FreedomIntelligence
/
ALLaVA-Phi2-2_7B

Text Generation
Transformers
Safetensors
English
llava_phi
custom_code
Model card Files Community
ALLaVA-Phi2-2_7B / README.md
g-h-chen's picture
g-h-chen
Update README.md
baa2743 verified
preview code
|
raw
history blame
9.35 kB
metadata
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

⚡ALLaVA is a project that provides a large-scale GPT4V-synthesized dataset for training LVLMs.⚡

📃 Paper • 🌐 Demo • 👨🏻‍💻 Github

🤗 ALLaVA-4V Dataset

🤗 ALLaVA-Phi3-mini-128k • 🤗 ALLaVA-StableLM2-1_6B • 🤗 ALLaVA-Phi2-2_7B

Benchmark Result

Our models ALLaVA-Phi3-mini-128k, ALLaVA-StableLM2-1_6B and 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.

🏭 Inference

All models can be loaded from 🤗 with .from_pretrained(). Check out the example scripts and make sure you have the same outputs as shown in the scripts.

🏋️‍♂️ Training

Data

training_datasets

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

Code

The training code is largely based on LLaVA-v1.5. We wholeheartedly express our gratitude for their invaluable contributions to open-sourcing LVLMs.

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. See here to prepare it for training.

🙌 Contributors

📝 Citation

If you find our data useful, please consider citing our work! We are FreedomIntelligence from Shenzhen Research Institute of Big Data and The Chinese University of Hong Kong, Shenzhen

@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}
}