Create README.md

README.md

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+---
+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-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.
+
+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:[email protected])
+- **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 torch
+
+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=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},
+}
+```