--- datasets: - lmms-lab/LLaVA-OneVision-Data language: - en - zh library_name: transformers license: apache-2.0 metrics: - accuracy tags: - multimodal model-index: - name: llava-onevision-qwen-7b-ov results: - task: type: multimodal dataset: name: AI2D type: ai2d metrics: - type: accuracy value: 81.4 name: accuracy verified: true - task: type: multimodal dataset: name: ChartQA type: chartqa metrics: - type: accuracy value: 80.0 name: accuracy verified: true - task: type: multimodal dataset: name: DocVQA type: docvqa metrics: - type: accuracy value: 90.2 name: accuracy verified: true - task: type: multimodal dataset: name: InfoVQA type: infovqa metrics: - type: accuracy value: 70.7 name: accuracy verified: true - task: type: multimodal dataset: name: MathVerse type: mathverse metrics: - type: accuracy value: 26.2 name: accuracy verified: true - task: type: multimodal dataset: name: MathVista type: mathvista metrics: - type: accuracy value: 63.2 name: accuracy verified: true - task: type: multimodal dataset: name: MMBench type: mmbench metrics: - type: accuracy value: 80.8 name: accuracy verified: true - task: type: multimodal dataset: name: MME-Perception type: mme-perception metrics: - type: score value: 1580 name: score verified: true - task: type: multimodal dataset: name: MME-Cognition type: mme-cognition metrics: - type: score value: 418 name: score verified: true - task: type: multimodal dataset: name: MMMU type: mmmu metrics: - type: accuracy value: 48.8 name: accuracy verified: true - task: type: multimodal dataset: name: MMVet type: mmvet metrics: - type: accuracy value: 57.5 name: accuracy verified: true - task: type: multimodal dataset: name: MMStar type: mmstar metrics: - type: accuracy value: 61.7 name: accuracy verified: true - task: type: multimodal dataset: name: Seed-Bench type: seed-bench metrics: - type: accuracy value: 75.4 name: accuracy verified: true - task: type: multimodal dataset: name: Science-QA type: science-qa metrics: - type: accuracy value: 96.0 name: accuracy verified: true - task: type: multimodal dataset: name: ImageDC type: imagedc metrics: - type: accuracy value: 88.9 name: accuracy verified: true - task: type: multimodal dataset: name: MMLBench type: mmlbench metrics: - type: accuracy value: 77.1 name: accuracy verified: true - task: type: multimodal dataset: name: RealWorldQA type: realworldqa metrics: - type: accuracy value: 66.3 name: accuracy verified: true - task: type: multimodal dataset: name: Vibe-Eval type: vibe-eval metrics: - type: accuracy value: 51.7 name: accuracy verified: true - task: type: multimodal dataset: name: LLaVA-W type: llava-w metrics: - type: accuracy value: 90.7 name: accuracy verified: true - task: type: multimodal dataset: name: LLaVA-Wilder type: l-wilder metrics: - type: accuracy value: 67.8 name: accuracy verified: true - task: type: multimodal dataset: name: ActNet-QA type: actnet-qa metrics: - type: accuracy value: 56.6 name: accuracy verified: true - task: type: multimodal dataset: name: EgoSchema type: egoschema metrics: - type: accuracy value: 60.1 name: accuracy verified: true - task: type: multimodal dataset: name: MLVU type: mlvu metrics: - type: accuracy value: 64.7 name: accuracy verified: true - task: type: multimodal dataset: name: MVBench type: mvbench metrics: - type: accuracy value: 56.7 name: accuracy verified: true - task: type: multimodal dataset: name: NextQA type: nextqa metrics: - type: accuracy value: 79.4 name: accuracy verified: true - task: type: multimodal dataset: name: PercepTest type: percepTest metrics: - type: accuracy value: 49.7 name: accuracy verified: true - task: type: multimodal dataset: name: SeedBench type: seedbench metrics: - type: accuracy value: 56.9 name: accuracy verified: true - task: type: multimodal dataset: name: VideoChatGPT type: videochatgpt metrics: - type: score value: 3.49 name: score verified: true - task: type: multimodal dataset: name: VideoDC type: videodc metrics: - type: score value: 3.75 name: score verified: true - task: type: multimodal dataset: name: VideoMME type: videomme metrics: - type: accuracy value: 58.2 name: accuracy verified: true --- # 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 The LLaVA-OneVision models are 0.5/7/72B parameter models trained on [LLaVA-OneVision](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data), based on Qwen2 language model with a context window of 32K tokens. - **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:** [Bo Li](mailto:drluodian@gmail.com) - **Languages:** English, Chinese ## 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 We provide the simple generation process for using our model. For more details, you could refer to [Github](https://github.com/LLaVA-VL/LLaVA-NeXT). ```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 torch import sys import warnings warnings.filterwarnings("ignore") pretrained = "lmms-lab/llava-onevision-qwen2-7b-ov" 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=torch.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="pt").unsqueeze(0).to(device) 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, ) 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 - **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}, } ```