Add pipeline tag, library name, and link to code
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by
						
nielsr
	
							HF Staff
						- opened
							
					
    	
        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-Qwen2.5-VL-7B) results from training Qwen2.5-VL-7B 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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            <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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            ---
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            base_model:
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            - Qwen/Qwen2.5-VL-7B-Instruct
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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-Qwen2.5-VL-7B) results from training Qwen2.5-VL-7B 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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            [[\ud83d\udcd6 Paper](https://arxiv.org/abs/2505.13886)] [[\ud83e\udd17 GameQA-140K Dataset](https://huggingface.co/datasets/Gabriel166/GameQA-140K)] [[\ud83e\udd17 GameQA-5K Dataset](https://huggingface.co/datasets/Code2Logic/GameQA-5K)] [[\ud83e\udd17 GameQA-InternVL3-8B](https://huggingface.co/Code2Logic/GameQA-InternVL3-8B) ] [[\ud83e\udd17 GameQA-Qwen2.5-VL-7B](https://huggingface.co/Code2Logic/GameQA-Qwen2.5-VL-7B)] [[\ud83e\udd17 GameQA-LLaVA-OV-7B](https://huggingface.co/Code2Logic/GameQA-llava-onevision-qwen2-7b-ov-hf) ]
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            Code: https://github.com/tongjingqi/Code2Logic
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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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