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README.md
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---
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datasets:
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- lmms-lab/LLaVA-OneVision-Data
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language:
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- en
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- zh
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library_name: transformers
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license: apache-2.0
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metrics:
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- accuracy
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tags:
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- multimodal
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---
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# LLaVA-OneVision
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![banner](https://i.postimg.cc/pL17YtG4/WX20240508-220230-2x.png)
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Play with the model on the [LLaVA OneVision Chat](https://llava-onevision.lmms-lab.com/).
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## Table of Contents
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1. [Model Summary](##model-summary)
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2. [Use](##use)
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3. [Limitations](##limitations)
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4. [Training](##training)
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5. [License](##license)
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6. [Citation](##citation)
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## Model Summary
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`llava-onevision-72b-ov-chat` is our latest model specifically designed for chat scenarios. It is built upon `llava-onevision-72b-ov` and has undergone iterative DPO training with human preference, making it well-suited for chat applications.
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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.
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For further details, please refer to our upcoming blog or paper.
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- **Repository:** [LLaVA-VL/LLaVA-NeXT](https://github.com/LLaVA-VL/LLaVA-NeXT?tab=readme-ov-file)
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- **Project Website:** [llava-onevision.lmms-lab.com](llava-onevision.lmms-lab.com)
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- **Paper:** [LLaVA-OneVision](arxiv.org/abs/2408.03326)
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- **Point of Contact:** [Tianyi Xiong](https://tyxiong23.github.io/), [Bo Li](mailto:[email protected])
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- **Languages:** English, Chinese
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## Benchmark Performance
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To be released
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## Use
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### Intended use
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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.
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**Feel free to share your generations in the Community tab!**
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### Generation
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```python
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# pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git
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from llava.model.builder import load_pretrained_model
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from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
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from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
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from llava.conversation import conv_templates, SeparatorStyle
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from PIL import Image
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import requests
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import copy
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import torch
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import sys
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import warnings
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warnings.filterwarnings("ignore")
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pretrained = "lmms-lab/llava-onevision-qwen2-0.5b-si"
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model_name = "llava_qwen"
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device = "cuda"
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device_map = "auto"
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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
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model.eval()
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url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
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image = Image.open(requests.get(url, stream=True).raw)
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image_tensor = process_images([image], image_processor, model.config)
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image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]
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conv_template = "qwen_1_5" # Make sure you use correct chat template for different models
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question = DEFAULT_IMAGE_TOKEN + "\nWhat is shown in this image?"
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conv = copy.deepcopy(conv_templates[conv_template])
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conv.append_message(conv.roles[0], question)
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conv.append_message(conv.roles[1], None)
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prompt_question = conv.get_prompt()
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input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
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image_sizes = [image.size]
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cont = model.generate(
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input_ids,
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images=image_tensor,
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image_sizes=image_sizes,
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do_sample=False,
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temperature=0,
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max_new_tokens=4096,
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)
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text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
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print(text_outputs)
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```
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# Training
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## Model
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- **Architecture:** SO400M + Qwen2
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- **Pretraining Stage:** LCS-558K, 1 epoch, projector
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- **Mid Stage:** A mixture of 4.7M high-quality synthetic data, 1 epoch, full model
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- **Final-Image Stage:** A mixture of 3.6M single-image data, 1 epoch, full model
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- **OneVision Stage:** A mixture of 1.6M single-image/multi-image/video data, 1 epoch, full model
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- **Precision:** bfloat16
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## Hardware & Software
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- **GPUs:** 256 \* Nvidia Tesla A100 (for whole model series training)
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- **Orchestration:** [Huggingface Trainer](https://huggingface.co/docs/transformers/main_classes/trainer)
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- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
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# Citation
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```
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@article{li2024llavaonevision,
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title={LLaVA-OneVision},
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}
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```
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