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license: mit
base_model:
- CodeGoat24/UnifiedReward-2.0-qwen35-4b
---
## Model Summary
`UnifiedReward-Think-qwen35-4b` is the first unified multimodal CoT reward model, capable of multi-dimensional, step-by-step long-chain reasoning for both visual understanding and generation reward tasks.
For further details, please refer to the following resources:
- 📰 Paper: https://arxiv.org/pdf/2505.03318
- 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/think
- 🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-models-67c3008148c3a380d15ac63a
- 🤗 Dataset Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-training-data-67c300d4fd5eff00fa7f1ede
- 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io)
## vLLM Server Deployment
```
export VLLM_DISABLE_FLASHINFER_GDN_PREFILL=1
export TOKENIZERS_PARALLELISM=false
vllm serve CodeGoat24/UnifiedReward-Think-qwen35-4b \
--host localhost \
--port 8080 \
--trust-remote-code \
--served-model-name UnifiedReward \
--gpu-memory-utilization 0.95 \
--mm-encoder-tp-mode data \
--mm-processor-cache-type shm \
--enable-prefix-caching \
--tensor-parallel-size 8 \
--default-chat-template-kwargs '{"enable_thinking": false}'
```
The inference code is provided [here](https://github.com/CodeGoat24/UnifiedReward/tree/main/UnifiedReward-Think/inference_qwen/UnifiedReward-Think-qwen3-inference).
## Citation
```
@article{unifiedreward-think,
title={Unified multimodal chain-of-thought reward model through reinforcement fine-tuning},
author={Wang, Yibin and Li, Zhimin and Zang, Yuhang and Wang, Chunyu and Lu, Qinglin and Jin, Cheng and Wang, Jiaqi},
journal={arXiv preprint arXiv:2505.03318},
year={2025}
}
``` |