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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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model-index: |
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- name: llava-onevision-qwen-7b-ov |
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results: |
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- task: |
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type: multimodal |
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dataset: |
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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: |
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name: DocVQA |
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type: docvqa |
|
metrics: |
|
- type: accuracy |
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value: 90.2 |
|
name: accuracy |
|
verified: true |
|
- task: |
|
type: multimodal |
|
dataset: |
|
name: InfoVQA |
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type: infovqa |
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metrics: |
|
- type: accuracy |
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value: 70.7 |
|
name: accuracy |
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verified: true |
|
- task: |
|
type: multimodal |
|
dataset: |
|
name: MathVerse |
|
type: mathverse |
|
metrics: |
|
- type: accuracy |
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value: 26.2 |
|
name: accuracy |
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verified: true |
|
- task: |
|
type: multimodal |
|
dataset: |
|
name: MathVista |
|
type: mathvista |
|
metrics: |
|
- type: accuracy |
|
value: 63.2 |
|
name: accuracy |
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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 |
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verified: true |
|
- task: |
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type: multimodal |
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dataset: |
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name: MME-Cognition |
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type: mme-cognition |
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metrics: |
|
- type: score |
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value: 418 |
|
name: score |
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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 |
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verified: true |
|
- task: |
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type: multimodal |
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dataset: |
|
name: MMStar |
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type: mmstar |
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metrics: |
|
- type: accuracy |
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value: 61.7 |
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name: accuracy |
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verified: true |
|
- task: |
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type: multimodal |
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dataset: |
|
name: Seed-Bench |
|
type: seed-bench |
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metrics: |
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- type: accuracy |
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value: 75.4 |
|
name: accuracy |
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verified: true |
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- task: |
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type: multimodal |
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dataset: |
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name: Science-QA |
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type: science-qa |
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metrics: |
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- type: accuracy |
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value: 96.0 |
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name: accuracy |
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verified: true |
|
- task: |
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type: multimodal |
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dataset: |
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name: ImageDC |
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type: imagedc |
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metrics: |
|
- type: accuracy |
|
value: 88.9 |
|
name: accuracy |
|
verified: true |
|
- task: |
|
type: multimodal |
|
dataset: |
|
name: MMLBench |
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type: mmlbench |
|
metrics: |
|
- type: accuracy |
|
value: 77.1 |
|
name: accuracy |
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verified: true |
|
- task: |
|
type: multimodal |
|
dataset: |
|
name: RealWorldQA |
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type: realworldqa |
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metrics: |
|
- type: accuracy |
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value: 66.3 |
|
name: accuracy |
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verified: true |
|
- task: |
|
type: multimodal |
|
dataset: |
|
name: Vibe-Eval |
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type: vibe-eval |
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metrics: |
|
- type: accuracy |
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value: 51.7 |
|
name: accuracy |
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verified: true |
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- task: |
|
type: multimodal |
|
dataset: |
|
name: LLaVA-W |
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type: llava-w |
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metrics: |
|
- type: accuracy |
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value: 90.7 |
|
name: accuracy |
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verified: true |
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- task: |
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type: multimodal |
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dataset: |
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name: LLaVA-Wilder |
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type: l-wilder |
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metrics: |
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- type: accuracy |
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value: 67.8 |
|
name: accuracy |
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verified: true |
|
- task: |
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type: multimodal |
|
dataset: |
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name: ActNet-QA |
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type: actnet-qa |
|
metrics: |
|
- type: accuracy |
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value: 56.6 |
|
name: accuracy |
|
verified: true |
|
- task: |
|
type: multimodal |
|
dataset: |
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name: EgoSchema |
|
type: egoschema |
|
metrics: |
|
- type: accuracy |
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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: |
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type: multimodal |
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dataset: |
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name: NextQA |
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type: nextqa |
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metrics: |
|
- type: accuracy |
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value: 79.4 |
|
name: accuracy |
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verified: true |
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- task: |
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type: multimodal |
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dataset: |
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name: PercepTest |
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type: percepTest |
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metrics: |
|
- type: accuracy |
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value: 49.7 |
|
name: accuracy |
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verified: true |
|
- task: |
|
type: multimodal |
|
dataset: |
|
name: SeedBench |
|
type: seedbench |
|
metrics: |
|
- type: accuracy |
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value: 56.9 |
|
name: accuracy |
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verified: true |
|
- task: |
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type: multimodal |
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dataset: |
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name: VideoChatGPT |
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type: videochatgpt |
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metrics: |
|
- type: score |
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value: 3.49 |
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name: score |
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verified: true |
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- task: |
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type: multimodal |
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dataset: |
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name: VideoDC |
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type: videodc |
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metrics: |
|
- type: score |
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value: 3.75 |
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name: score |
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verified: true |
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- task: |
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type: multimodal |
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dataset: |
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name: VideoMME |
|
type: videomme |
|
metrics: |
|
- type: accuracy |
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value: 58.2 |
|
name: accuracy |
|
verified: true |
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--- |
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|
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# LLaVA-OneVision |
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 |
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|
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Play with the model on the [LLaVA OneVision Chat](https://llava-onevision.lmms-lab.com/). |
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|
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## Table of Contents |
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|
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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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|
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## Model Summary |
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|
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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. |
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|
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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:** [Bo Li](mailto:[email protected]) |
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- **Languages:** English, Chinese |
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|
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|
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## Use |
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### Intended use |
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|
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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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|
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### Generation |
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|
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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). |
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|
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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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|
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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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|
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import sys |
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import warnings |
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|
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warnings.filterwarnings("ignore") |
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pretrained = "lmms-lab/llava-onevision-qwen2-7b-ov" |
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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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|
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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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|
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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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|
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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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|
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|
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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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|
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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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``` |
