20 lines
880 B
Markdown
20 lines
880 B
Markdown
|
---
|
||
|
|
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
|