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# CogAgent
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**CogAgent** is an open-source visual language model improved based on **CogVLM**. CogAgent-18B has 11 billion visual and 7 billion language parameters.
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📖 Paper: https://arxiv.org/abs/2312.08914
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🚀 GitHub: For more information such as demo, fine-tuning, and query prompts, please refer to [Our GitHub](https://github.com/THUDM/CogVLM/)
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CogAgent demonstrates **strong performance** in image understanding and GUI agent:
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1. CogAgent-18B **achieves state-of-the-art generalist performance on 9 cross-modal benchmarks**, including: VQAv2, MM-Vet, POPE, ST-VQA, OK-VQA, TextVQA, ChartQA, InfoVQA, DocVQA.
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# CogAgent
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**CogAgent** is an open-source visual language model improved based on **CogVLM**.
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📖 Paper: https://arxiv.org/abs/2312.08914
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🚀 GitHub: For more information such as demo, fine-tuning, and query prompts, please refer to [Our GitHub](https://github.com/THUDM/CogVLM/)
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## Reminder
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**This is the ``cogagent-chat`` version of CogAgent checkpoint.**
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We have open-sourced two versions of CogAgent checkpoints, and you can choose one based on your needs.
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1. ``cogagent-chat``: This model has strong capabilities in **GUI Agent, visual multi-turn dialogue, visual grounding,** etc.
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If you need GUI Agent and Visual Grounding functions, or need to conduct multi-turn dialogues with a given image, we recommend using this version of the model.
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3. ``cogagent-vqa``: This model has *stronger* capabilities in **single-turn visual dialogue**.
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If you need to **work on VQA leaderboards** (such as MMVET, VQAv2), we recommend using this model.
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## Introduction
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CogAgent-18B has 11 billion visual and 7 billion language parameters.
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CogAgent demonstrates **strong performance** in image understanding and GUI agent:
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1. CogAgent-18B **achieves state-of-the-art generalist performance on 9 cross-modal benchmarks**, including: VQAv2, MM-Vet, POPE, ST-VQA, OK-VQA, TextVQA, ChartQA, InfoVQA, DocVQA.
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