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https://github.com/feizc/Visual-LLaMA
Open LLaMA Eyes to See the World
https://github.com/feizc/Visual-LLaMA
Last synced: about 1 month ago
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Open LLaMA Eyes to See the World
- Host: GitHub
- URL: https://github.com/feizc/Visual-LLaMA
- Owner: feizc
- Created: 2023-04-03T08:52:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-04-16T07:20:49.000Z (over 1 year ago)
- Last Synced: 2024-08-03T01:13:17.846Z (4 months ago)
- Language: Python
- Size: 171 KB
- Stars: 168
- Watchers: 6
- Forks: 10
- Open Issues: 2
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-llm-and-aigc - feizc/Visual-LLaMA - LLaMA?style=social"/> : Open LLaMA Eyes to See the World. This project aims to optimize LLaMA model for visual information understanding like GPT-4 and further explore the potentional of large language model. (Summary)
- awesome-llm-and-aigc - feizc/Visual-LLaMA - LLaMA?style=social"/> : Open LLaMA Eyes to See the World. This project aims to optimize LLaMA model for visual information understanding like GPT-4 and further explore the potentional of large language model. (Summary)
README
## Open LLaMA Eyes to See the World
This project aims to optimize LLaMA model for visual information understanding like GPT-4 and further explore the potentional of large language model.
Generally, we use CLIP vision encoder to extract image features, then image features are projected with MLP-based or Transformer-based connection network into text embedding dimensionality. Then, visual representation (including additional special tokens [boi] and [eoi]) is concatenated with text representation to learn in a autoregressive manner. The framework is similar to [kosmos-1](https://arxiv.org/pdf/2302.14045.pdf) and [PaLM-E](https://palm-e.github.io/).
- [X] Code adjustation to support for multi-modal generation. Download [clip](https://huggingface.co/openai/clip-vit-large-patch14) and [LLaMA](https://huggingface.co/decapoda-research/llama-7b-hf) models from huggingface. Meantime, we test the scripts are also compatible with other LLaMA model size. Please use script ```preprocess.py``` to deal with the data.
- [X] Supervised training stage: freeze llama and clip-encoder models and only optimize the connection network. In this stage, we use COCO, CC-3M and COYO-700M datasets with training scripts ```train.py```.
We provide the training hyper-parameter used in our experiemnts on A100 GPU(80G). We also evaluate the image captioning performance in COCO testing set.
| Argument | Values |
|------|------|
| `batch size` | 1 * 8 * 8 |
| `epochs` | 3 |
| `cut length` | 256 |
| `learning rate` | 4e-3 |
| `image sequence length` | 10 |
- [X] Instructing tuning stage: fine-tuning full model with mixed VQA and language-only instructing dataset. We use lora strategy to optimize the entire model with fine-tuning scripts ```finetune.py```.
| Argument | Values |
|------|------|
| `batch size` | 1024 |
| `epochs` | 3 |
| `cut length` | 256 |
| `learning rate` | 2e-5 |
| `image sequence length` | 10 |
- [ ] Open source trained ckpt on huggingface and gradio interface for multi-model generation.
## Reference
[1] https://github.com/facebookresearch/llama
[2] https://github.com/tloen/alpaca-lora