初始化项目，由ModelHub XC社区提供模型

Model: RenlyH/CodeV-RL
Source: Original Platform

README.md

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+---
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
+datasets:
+- RenlyH/CodeV-RL-Data
+language:
+- en
+- zh
+license: mit
+metrics:
+- accuracy
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
+
+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.
+
+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).
+
+Code: https://github.com/RenlyH/CodeV