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UnifiedReward-Think-qwen-7b

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 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-## Model Details
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 ---
+license: mit
+datasets:
+- CodeGoat24/HPD
+- CodeGoat24/OIP
+- CodeGoat24/EvalMuse
+- CodeGoat24/ShareGPTVideo-DPO
+- CodeGoat24/LLaVA-Critic-113k
+- CodeGoat24/VideoDPO
+- CodeGoat24/Text-2-Video-Human-Preferences
+- CodeGoat24/OpenAI-4o_t2i_human_preference
+- CodeGoat24/ImageGen_Reward_Cold_Start
+base_model:
+- CodeGoat24/UnifiedReward-qwen-7b
 ---
+## Model Summary
+`Unified-Reward-Think-qwen-7b` 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)
+### Quick Start
+All inference codes are provided in our [github](https://github.com/CodeGoat24/UnifiedReward/tree/main/UnifiedReward-Think).
+We take image understanding assessment as example here:
+~~~python
+import json
+import random
+import torch
+import tqdm
+from PIL import Image
+import warnings
+import os
+from transformers import AutoProcessor, AutoTokenizer, Qwen2_5_VLForConditionalGeneration
+from qwen_vl_utils import process_vision_info
+warnings.filterwarnings("ignore")
+model_path = "CodeGoat24/UnifiedReward-Think-qwen-7b"
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    model_path, torch_dtype="auto", device_map="auto"
+)
+processor = AutoProcessor.from_pretrained(model_path)
+url = "https://github.com/LLaVA-VL/blog/blob/main/2024-10-03-llava-critic/static/images/critic_img_seven.png?raw=True"
+image = Image.open(requests.get(url, stream=True).raw)
+prompt_text = ("Given a question and a reference image, please analyze in detail the two provided answers (Answer 1 and Answer 2). " \
+            "Evaluate them based on the following three core dimensions:\n" \
+            "1. Semantic accuracy: How well the answer reflects the visual content of the image\n" \
+            "2. Correctness: Whether the answer is logically and factually correct\n" \
+            "3. Clarity: Whether the answer is clearly and fluently expressed\n" \
+            "You may also consider additional dimensions if you find them relevant (e.g., reasoning ability, attention to detail, multimodal grounding, etc.). " \
+            "For each dimension, provide a score from 1 to 10 for both answers, and briefly explain your reasoning. " \
+            "Then, compute the total score for each answer by explicitly adding the scores for all dimensions and showing the full calculation. " \
+            "Enclose your full reasoning within <think> and </think> tags. " \
+            "Then, in the <answer> tag, output exactly one of the following: 'Answer 1 is better' or 'Answer 2 is better'. No other text is allowed in the <answer> section.\n\n" \
+            "Example format:\n" \
+            "<think>\n" \
+            "1. Semantic accuracy: Answer 1 (9/10) - ...; Answer 2 (7/10) - ...\n" \
+            "2. Correctness: Answer 1 (8/10) - ...; Answer 2 (7/10) - ...\n" \
+            "3. Clarity: Answer 1 (9/10) - ...; Answer 2 (8/10) - ...\n" \
+            "[Additional dimensions if any]: Answer 1 (6/10) - ...; Answer 2 (7/10) - ...\n" \
+            "Total score:\nAnswer 1: 9+8+9+6=32\nAnswer 2: 7+7+8+7=29\n" \
+            "</think>\n" \
+            "<answer>Answer 1 is better</answer>\n\n" \
+            "**Note: In the example above, scores and the final answer are placeholders meant only to demonstrate the format. Your actual evaluation should be based on the quality of two given answers.**\n\n"
+            f"Your task is provided as follows:\nQuestion: [{Query}]\nAnswer 1: [{R1}]\nAnswer 2: [{R2}]")
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": prompt_text},
+        ],
+    }
+]
+chat_input = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[chat_input],
+    images=image_inputs,
+    videos=video_inputs,
+    return_tensors="pt",
+    padding=True
+).to("cuda")
+with torch.no_grad():
+    generated_ids = model.generate(**inputs, max_new_tokens=4096)
+generated_trimmed = [
+    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output = processor.batch_decode(generated_trimmed, skip_special_tokens=True)[0]
+print(output)
+~~~
+## 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}
+}
+```