Metadata-Version: 2.4 Name: voco_llama Version: 1.2.2.post1 Summary: Towards vision compression with large language models. Project-URL: Homepage, https://yxxxb.github.io/VoCo-LLaMA-page/ Project-URL: Bug Tracker, https://github.com/Yxxxb/VoCo-LLaMA/issues Classifier: Programming Language :: Python :: 3 Classifier: License :: OSI Approved :: Apache Software License Requires-Python: >=3.8 Description-Content-Type: text/markdown License-File: LICENSE Requires-Dist: torch Requires-Dist: torchvision Requires-Dist: transformers==4.37.2 Requires-Dist: tokenizers==0.15.1 Requires-Dist: sentencepiece==0.1.99 Requires-Dist: shortuuid Requires-Dist: accelerate==0.21.0 Requires-Dist: peft==0.6.0 Requires-Dist: bitsandbytes Requires-Dist: pydantic Requires-Dist: markdown2[all] Requires-Dist: numpy Requires-Dist: scikit-learn==1.2.2 Requires-Dist: gradio==4.16.0 Requires-Dist: gradio_client==0.8.1 Requires-Dist: requests Requires-Dist: httpx==0.24.0 Requires-Dist: uvicorn Requires-Dist: fastapi Requires-Dist: einops==0.6.1 Requires-Dist: einops-exts==0.0.4 Requires-Dist: timm==0.6.13 Provides-Extra: train Requires-Dist: deepspeed; extra == "train" Requires-Dist: ninja; extra == "train" Requires-Dist: wandb; extra == "train" Provides-Extra: build Requires-Dist: build; extra == "build" Requires-Dist: twine; extra == "build" Dynamic: license-file # VoCo-LLaMA: Towards Vision Compression with Large Language Models [Xubing Ye](https://yxxxb.github.io/), [Yukang Gan](https://scholar.google.com/citations?user=8rltp9AAAAAJ&hl=zh-CN), [Xiaoke Huang](https://xk-huang.github.io/), [Yixiao Ge](https://geyixiao.com/), [Yansong Tang](https://andytang15.github.io)

## TL;DR We propose VoCo-LLaMA, the first approach to compress vision tokens using LLMs. By fully utilizing the LLMs' understanding paradigm of vision tokens, our method can compress hundreds of vision tokens into a single VoCo token, while minimizing visual information loss. VoCo-LLaMA demonstrates the ability to understand video through continuous training using time-series compressed token sequences of video frames. VoCo-LLaMA presents a promising way to unlock the full potential of VLMs' contextual window. ![image](https://i.imgur.com/wznshA6.jpeg) ## News - [x] **[2024/06/17]** Upload paper and release vision compression code. ## Preparation ### Install 1. Clone this repository and navigate to VoCo-LLaMA folder ```bash git clone https://github.com/Yxxxb/VoCo-LLaMA.git cd VoCo-LLaMA ``` 2. Install Package ```Shell conda create -n voco_llama python=3.10 -y conda activate voco_llama pip install --upgrade pip # enable PEP 660 support pip install -e . ``` 3. Install additional packages for training cases ``` pip install -e ".[train]" pip install flash-attn --no-build-isolation cp VoCo-LLaMA/llava/model/language_model/cache_py/modeling_attn_mask_utils.py /data/miniconda3/envs/voco_llama/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py ``` ### Data and Pre-trained weights VoCo-LLaMA training requires only visual instruction fine-tuning. Please download the aligned LLaVA checkpoints ([base LLM and projection layers](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). Please download the annotation of the LLaVA instruction tuning data [llava_v1_5_mix665k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json), and download the images from constituting datasets: - COCO: [train2017](http://images.cocodataset.org/zips/train2017.zip) - GQA: [images](https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip) - OCR-VQA: [download script](https://drive.google.com/drive/folders/1_GYPY5UkUy7HIcR0zq3ZCFgeZN7BAfm_?usp=sharing), we save all files as `.jpg` - TextVQA: [train_val_images](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip) - VisualGenome: [part1](https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip), [part2](https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip) After downloading all of them, organize the data as follows in `./playground/data`, ``` ├── coco │ └── train2017 ├── gqa │ └── images ├── ocr_vqa │ └── images ├── textvqa │ └── train_images └── vg ├── VG_100K └── VG_100K_2 ``` ## Train VoCo-LLaMA is trained on 8 A100 GPUs with 40GB memory. To train on fewer GPUs, you can reduce the `per_device_train_batch_size` and increase the `gradient_accumulation_steps` accordingly. Always keep the global batch size the same: `per_device_train_batch_size` x `gradient_accumulation_steps` x `num_gpus`. Train VoCo-LLaMA with vision instruction tuning by running following command: ``` bash scripts/finetune.sh ``` ## Evaluation There are evaluations about visual understanding we follow the relevant settings in LLaVA. Please refer to the LLaVA official [repository](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md) for details of data setup and testing. ## Citation If you find this work useful, please consider citing our paper: ```bash @article{ye2024voco, author={Ye, Xubing and Gan, Yukang and Huang, Xiaoke and Ge, Yixiao and Shan, Ying and Tang, Yansong}, title={{VoCo-LLaMA: Towards Vision Compression with Large Language Models}}, journal={arXiv preprint arXiv:2406.12275}, year={2024}, } ``` ## ## Acknowledgement - [LLaVA](https://github.com/haotian-liu/LLaVA): the codebase we built upon. - [Vicuna](https://github.com/lm-sys/FastChat): our base model Vicuna-7B that has the amazing language capabilities!