# llava-onevision-qwen2-7b-ov-chat --- ## README([From Huggingface](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov-chat)) --- datasets: - lmms-lab/LLaVA-OneVision-Data language: - en - zh library_name: transformers license: apache-2.0 metrics: - accuracy tags: - multimodal --- # LLaVA-OneVision ![![banner](https://i.postimg.cc/pL17YtG4/WX20240508-220230-2x.png) Play with the model on the [LLaVA OneVision Chat](https://llava-onevision.lmms-lab.com/). ## Table of Contents 1. [Model Summary](##model-summary) 2. [Use](##use) 3. [Limitations](##limitations) 4. [Training](##training) 5. [License](##license) 6. [Citation](##citation) ## Model Summary `llava-onevision-7b-ov-chat` is our latest model specifically designed for chat scenarios. It is built upon `llava-onevision-7b-ov` and has undergone iterative DPO training with human preference, making it well-suited for chat applications. Research by [Tianyi Xiong](https://tyxiong23.github.io/) indicates that our iterative DPO training method enhances the model's chat capabilities while preserving its instruction-following abilities. For further details, please refer to our upcoming blog or paper. - **Repository:** [LLaVA-VL/LLaVA-NeXT](https://github.com/LLaVA-VL/LLaVA-NeXT?tab=readme-ov-file) - **Project Website:** [llava-onevision.lmms-lab.com](llava-onevision.lmms-lab.com) - **Paper:** [LLaVA-OneVision](arxiv.org/abs/2408.03326) - **Point of Contact:** [Tianyi Xiong](https://tyxiong23.github.io/), [Bo Li](mailto:drluodian@gmail.com) - **Languages:** English, Chinese ## Benchmark Performance To be released ## Use ### Intended use The model was trained on [LLaVA-OneVision Dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data) and have the ability to interact with images, multi-image and videos. **Feel free to share your generations in the Community tab!** ### Generation ```python # pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git from llava.model.builder import load_pretrained_model from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX from llava.conversation import conv_templates, SeparatorStyle from PIL import Image import requests import copy import paddle import sys import warnings warnings.filterwarnings("ignore") pretrained = "lmms-lab/llava-onevision-qwen2-0.5b-si" model_name = "llava_qwen" device = "cuda" device_map = "auto" tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map) # Add any other thing you want to pass in llava_model_args model.eval() url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true" image = Image.open(requests.get(url, stream=True).raw) image_tensor = process_images([image], image_processor, model.config) image_tensor = [_image.to(dtype=paddle.float16, device=device) for _image in image_tensor] conv_template = "qwen_1_5" # Make sure you use correct chat template for different models question = DEFAULT_IMAGE_TOKEN + "\nWhat is shown in this image?" conv = copy.deepcopy(conv_templates[conv_template]) conv.append_message(conv.roles[0], question) conv.append_message(conv.roles[1], None) prompt_question = conv.get_prompt() input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pd").unsqueeze(0) image_sizes = [image.size] cont = model.generate( input_ids, images=image_tensor, image_sizes=image_sizes, do_sample=False, temperature=0, max_new_tokens=4096, )[0] text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True) print(text_outputs) ``` # Training ## Model - **Architecture:** SO400M + Qwen2 - **Pretraining Stage:** LCS-558K, 1 epoch, projector - **Mid Stage:** A mixture of 4.7M high-quality synthetic data, 1 epoch, full model - **Final-Image Stage:** A mixture of 3.6M single-image data, 1 epoch, full model - **OneVision Stage:** A mixture of 1.6M single-image/multi-image/video data, 1 epoch, full model - **Critic / Preference Learning Stage:** 9.4k question-image input from [LLaVA-RLHF](https://llava-rlhf.github.io/) with self-generated responses, reward signal from [llava-critic-7b](https://huggingface.co/lmms-lab/llava-critic-7b), iterative DPO for 3 rounds, full model - **Precision:** bfloat16 ## Hardware & Software - **GPUs:** 256 \* Nvidia Tesla A100 (for whole model series training) - **Orchestration:** [Huggingface Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) # Citation ``` @article{li2024llavaonevision, title={LLaVA-OneVision: Easy Visual Task Transfer}, author={Li, Bo and Zhang, Yuanhan and Guo, Dong and Zhang, Renrui and Li, Feng and Zhang, Hao and Zhang, Kaichen and Li, Yanwei and Liu, Ziwei and Li, Chunyuan}, journal={arXiv preprint arXiv:2408.03326}, year={2024} } @article{xiong2024llavacritic, title={LLaVA-Critic: Learning to Evaluate Multimodal Models}, author={Xiong, Tianyi and Wang, Xiyao and Guo, Dong and Ye, Qinghao and Fan, Haoqi and Gu, Quanquan and Huang, Heng and Li, Chunyuan}, year={2024}, eprint={2410.02712}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2410.02712}, } ``` ## Model Files - [README.md](https://paddlenlp.bj.bcebos.com/models/community/lmms-lab/llava-onevision-qwen2-7b-ov-chat/README.md) (5.2 KB) - [added_tokens.json](https://paddlenlp.bj.bcebos.com/models/community/lmms-lab/llava-onevision-qwen2-7b-ov-chat/added_tokens.json) (101.0 B) - [config.json](https://paddlenlp.bj.bcebos.com/models/community/lmms-lab/llava-onevision-qwen2-7b-ov-chat/config.json) (3.0 KB) - [generation_config.json](https://paddlenlp.bj.bcebos.com/models/community/lmms-lab/llava-onevision-qwen2-7b-ov-chat/generation_config.json) (254.0 B) - [merges.txt](https://paddlenlp.bj.bcebos.com/models/community/lmms-lab/llava-onevision-qwen2-7b-ov-chat/merges.txt) (1.6 MB) - [model-00001-of-00004.safetensors](https://paddlenlp.bj.bcebos.com/models/community/lmms-lab/llava-onevision-qwen2-7b-ov-chat/model-00001-of-00004.safetensors) (4.5 GB) - [model-00002-of-00004.safetensors](https://paddlenlp.bj.bcebos.com/models/community/lmms-lab/llava-onevision-qwen2-7b-ov-chat/model-00002-of-00004.safetensors) (4.6 GB) - [model-00003-of-00004.safetensors](https://paddlenlp.bj.bcebos.com/models/community/lmms-lab/llava-onevision-qwen2-7b-ov-chat/model-00003-of-00004.safetensors) (4.7 GB) - [model-00004-of-00004.safetensors](https://paddlenlp.bj.bcebos.com/models/community/lmms-lab/llava-onevision-qwen2-7b-ov-chat/model-00004-of-00004.safetensors) (1.2 GB) - [model.safetensors.index.json](https://paddlenlp.bj.bcebos.com/models/community/lmms-lab/llava-onevision-qwen2-7b-ov-chat/model.safetensors.index.json) (78.9 KB) - [special_tokens_map.json](https://paddlenlp.bj.bcebos.com/models/community/lmms-lab/llava-onevision-qwen2-7b-ov-chat/special_tokens_map.json) (367.0 B) - [tokenizer_config.json](https://paddlenlp.bj.bcebos.com/models/community/lmms-lab/llava-onevision-qwen2-7b-ov-chat/tokenizer_config.json) (1.5 KB) - [trainer_state.json](https://paddlenlp.bj.bcebos.com/models/community/lmms-lab/llava-onevision-qwen2-7b-ov-chat/trainer_state.json) (91.6 KB) - [training_args.bin](https://paddlenlp.bj.bcebos.com/models/community/lmms-lab/llava-onevision-qwen2-7b-ov-chat/training_args.bin) (8.0 KB) - [vocab.json](https://paddlenlp.bj.bcebos.com/models/community/lmms-lab/llava-onevision-qwen2-7b-ov-chat/vocab.json) (2.6 MB) [Back to Main](../../)