Update README.md

README.md

@@ -1,199 +1,119 @@
 ---
-library_name: transformers
-tags: []
+license: mit
+datasets:
+- CodeGoat24/HPD
+- CodeGoat24/LiFT-HRA
+- CodeGoat24/OIP
+- CodeGoat24/EvalMuse
+- CodeGoat24/ShareGPTVideo-DPO
+- CodeGoat24/VideoFeedback
+- CodeGoat24/LLaVA-Critic-113k
+- CodeGoat24/VideoDPO
+base_model:
+- Qwen/Qwen2.5-VL-32B-Instruct
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+# UnifiedReward-qwen-32B
+We are actively gathering feedback from the community to improve our models. **We welcome your input and encourage you to stay updated through our repository**!!
+
+## Model Summary
+
+`UnifiedReward-qwen-32b` is the first unified reward model based on [Qwen/Qwen2.5-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-32B-Instruct) for multimodal understanding and generation assessment, enabling both pairwise ranking and pointwise scoring, which can be employed for vision model preference alignment.
+
+For further details, please refer to the following resources:
+- 📰 Paper: https://arxiv.org/pdf/2503.05236
+- 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/
+- 🤗 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)
+
+
+## 🏁 Compared with Current Reward Models
+
+|  Reward Model | Method| Image Generation | Image Understanding | Video Generation | Video Understanding
+| :-----: | :-----: |:-----: |:-----: | :-----: | :-----: |
+|  [PickScore](https://github.com/yuvalkirstain/PickScore) |Point | √ |  | ||
+|  [HPS](https://github.com/tgxs002/HPSv2) | Point | √ |  |||
+|  [ImageReward](https://github.com/THUDM/ImageReward) |  Point| √|  |||
+|  [LLaVA-Critic](https://huggingface.co/lmms-lab/llava-critic-7b) | Pair/Point | | √  |||
+|  [IXC-2.5-Reward](https://github.com/InternLM/InternLM-XComposer) | Pair/Point | | √  ||√|
+|  [VideoScore](https://github.com/TIGER-AI-Lab/VideoScore) | Point |  |  |√ ||
+|  [LiFT](https://github.com/CodeGoat24/LiFT) | Point |  |  |√| |
+|  [VisionReward](https://github.com/THUDM/VisionReward) | Point |√  | |√||
+|  [VideoReward](https://github.com/KwaiVGI/VideoAlign) | Point |  |  |√ ||
+|  UnifiedReward (Ours) | Pair/Point | √ | √ |√|√|
+
+
+### Quick Start
+All pair rank and point score inference codes are provided in our [github](https://github.com/CodeGoat24/UnifiedReward).
+
+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-qwen-32b"
+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 = f'Given an image and a corresponding question, please serve as an unbiased and fair judge to evaluate the quality of the answers provided by a Large Multimodal Model (LMM). Determine which answer is better and explain your reasoning with specific details. Your task is provided as follows:\nQuestion: [What this image presents?]\nThe first response: [The image is a black and white sketch of a line that appears to be in the shape of a cross. The line is a simple and straightforward representation of the cross shape, with two straight lines intersecting at a point.]\nThe second response: [This is a handwritten number seven.]\nASSISTANT:\n'
+
+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,
+  title={Unified Reward Model for Multimodal Understanding and Generation.},
+  author={Wang, Yibin and Zang, Yuhang, and Li, Hao and Jin, Cheng and Wang Jiaqi},
+  journal={arXiv preprint arXiv:2503.05236},
+  year={2025}
+}
+```