Improve model card: Add pipeline tag, library name, and code link (#1)
Browse files- Improve model card: Add pipeline tag, library name, and code link (8c377f3b9292c75083af5c2724dee16b5f4890d6)
Co-authored-by: Niels Rogge <[email protected]>
README.md
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datasets:
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- Code2Logic/GameQA-140K
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- Code2Logic/GameQA-5K
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***This model (GameQA-InternVL2.5-8B) results from training InternVL2.5-8B with GRPO solely on our [GameQA-5K](https://huggingface.co/datasets/Code2Logic/GameQA-5K) (sampled from the full [GameQA-140K](https://huggingface.co/datasets/Gabriel166/GameQA-140K) dataset).***
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This is the first work, to the best of our knowledge, that leverages ***game code*** to synthesize multimodal reasoning data for ***training*** VLMs. Furthermore, when trained with a GRPO strategy solely on **GameQA** (synthesized via our proposed **Code2Logic** approach), multiple cutting-edge open-source models exhibit significantly enhanced out-of-domain generalization.
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[[π Paper](https://arxiv.org/abs/2505.13886)] [[π€ GameQA-140K Dataset](https://huggingface.co/datasets/Gabriel166/GameQA-140K)] [[π€ GameQA-5K Dataset](https://huggingface.co/datasets/Code2Logic/GameQA-5K)] [[π€ GameQA-InternVL3-8B](https://huggingface.co/Code2Logic/GameQA-InternVL3-8B) ] [[π€ GameQA-Qwen2.5-VL-7B](https://huggingface.co/Code2Logic/GameQA-Qwen2.5-VL-7B)] [[π€ GameQA-LLaVA-OV-7B](https://huggingface.co/Code2Logic/GameQA-llava-onevision-qwen2-7b-ov-hf) ]
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<div align=center><img src="https://raw.githubusercontent.com/tongjingqi/Code2Logic/refs/heads/main/assets/categorized_30_games_images.png"></div>
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base_model:
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- OpenGVLab/InternVL2_5-8B
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datasets:
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- Code2Logic/GameQA-140K
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- Code2Logic/GameQA-5K
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license: apache-2.0
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pipeline_tag: image-text-to-text
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library_name: transformers
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***This model (GameQA-InternVL2.5-8B) results from training InternVL2.5-8B with GRPO solely on our [GameQA-5K](https://huggingface.co/datasets/Code2Logic/GameQA-5K) (sampled from the full [GameQA-140K](https://huggingface.co/datasets/Gabriel166/GameQA-140K) dataset).***
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This is the first work, to the best of our knowledge, that leverages ***game code*** to synthesize multimodal reasoning data for ***training*** VLMs. Furthermore, when trained with a GRPO strategy solely on **GameQA** (synthesized via our proposed **Code2Logic** approach), multiple cutting-edge open-source models exhibit significantly enhanced out-of-domain generalization.
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[[π Paper](https://arxiv.org/abs/2505.13886)] [[π» Code](https://github.com/tongjingqi/Code2Logic)] [[π€ GameQA-140K Dataset](https://huggingface.co/datasets/Gabriel166/GameQA-140K)] [[π€ GameQA-5K Dataset](https://huggingface.co/datasets/Code2Logic/GameQA-5K)] [[π€ GameQA-InternVL3-8B](https://huggingface.co/Code2Logic/GameQA-InternVL3-8B) ] [[π€ GameQA-Qwen2.5-VL-7B](https://huggingface.co/Code2Logic/GameQA-Qwen2.5-VL-7B)] [[π€ GameQA-LLaVA-OV-7B](https://huggingface.co/Code2Logic/GameQA-llava-onevision-qwen2-7b-ov-hf) ]
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<div align=center><img src="https://raw.githubusercontent.com/tongjingqi/Code2Logic/refs/heads/main/assets/categorized_30_games_images.png"></div>
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