20 lines
880 B
Markdown
20 lines
880 B
Markdown
---
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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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- RenlyH/CodeV-RL-Data
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language:
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- en
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- zh
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license: mit
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metrics:
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- accuracy
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pipeline_tag: image-text-to-text
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library_name: transformers
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---
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CodeV is a code-based visual agent trained with Tool-Aware Policy Optimization (TAPO) for faithful visual reasoning. This agentic vision-language model is designed to "think with images" by calling image operations, addressing unfaithful visual reasoning in prior models. CodeV achieves competitive accuracy and substantially increases faithful tool-use rates on visual search benchmarks, also demonstrating strong performance on multimodal reasoning and math benchmarks.
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This model was presented in the paper [CodeV: Code with Images for Faithful Visual Reasoning via Tool-Aware Policy Optimization](https://huggingface.co/papers/2511.19661).
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Code: https://github.com/RenlyH/CodeV |