Improve model card with detailed description, usage, and additional info

README.md

@@ -4,10 +4,129 @@ base_model:
 datasets:
 - WaltonFuture/Multimodal-Cold-Start
 - WaltonFuture/Multimodal-RL-Data
+library_name: transformers
 license: apache-2.0
 pipeline_tag: image-text-to-text
-library_name: transformers
 ---
 
-* 🐙 **GitHub Repo:** [waltonfuture/RL-with-Cold-Start](https://github.com/waltonfuture/RL-with-Cold-Start)
-* 📜 **Paper (arXiv):** [Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start (arXiv:2505.22334)](https://arxiv.org/abs/2505.22334)
+# Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start
+
+*   🐙 **GitHub Repo:** [waltonfuture/RL-with-Cold-Start](https://github.com/waltonfuture/RL-with-Cold-Start)
+*   📜 **Paper (arXiv):** [Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start (arXiv:2505.22334)](https://arxiv.org/abs/2505.22334)
+
+## Introduction
+
+This model is presented in the paper "Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start". We present a comprehensive study on enhancing multimodal reasoning through a two-stage approach: (1) supervised fine-tuning (SFT) as a cold start with structured chain-of-thought reasoning patterns, followed by (2) reinforcement learning via GRPO to further refine these capabilities.
+
+Our extensive experiments show that this combined approach consistently outperforms both SFT-only and RL-only methods across challenging multimodal reasoning benchmarks. The resulting models achieve state-of-the-art performance among open-source MLLMs at both 3B and 7B scales, with our 7B model showing substantial improvements over base models (e.g., 66.3%→73.4% on MathVista, 62.9%→70.4% on We-Math) and our 3B model achieving performance competitive with several 7B models.
+
+<div align=center>
+<img src="https://huggingface.co/WaltonFuture/Qwen2.5VL-3b-RLCS/resolve/main/model_comparison.png" width = "80%" alt="Model Comparison" align=center/>
+</div>
+
+### ✨ Key Highlights
+
+*   **Two-Stage Approach:** Combines Supervised Fine-Tuning (SFT) as a "cold start" for structured chain-of-thought reasoning with Reinforcement Learning (RL) via GRPO for further refinement.
+*   **Enhanced Multimodal Reasoning:** Consistently outperforms both SFT-only and RL-only methods on challenging multimodal reasoning benchmarks.
+*   **State-of-the-Art Performance:** Achieves SOTA performance among open-source MLLMs at both 3B and 7B scales.
+*   **Significant Improvements:** The 7B model shows substantial gains (e.g., 73.4% on MathVista, 70.4% on We-Math) over base models, while the 3B model is competitive with several 7B models.
+*   **Practical Guidance:** Provides practical insights for developing advanced multimodal reasoning models.
+
+## Sample Usage
+
+You can easily load and use this model with the Hugging Face `transformers` library. Ensure you have `transformers` and `Pillow` installed.
+
+```bash
+pip install transformers Pillow
+```
+
+Below is an example demonstrating how to perform multimodal inference:
+
+```python
+from transformers import AutoProcessor, AutoModelForCausalLM
+from PIL import Image
+import torch
+
+# Load the model and processor
+# Replace "WaltonFuture/Qwen2.5VL-3b-RLCS" with "WaltonFuture/Qwen2.5VL-7b-RLCS" for the 7B model.
+model_id = "WaltonFuture/Qwen2.5VL-3b-RLCS" 
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
+
+# Example image (replace with your image path or a PIL Image object)
+# Make sure to provide a valid image path.
+# For example, download an image locally:
+# import requests
+# from io import BytesIO
+# image_url = "https://www.ilusionviajera.com/wp-content/uploads/2021/04/paris-eiffel-tower-in-spring.jpg"
+# response = requests.get(image_url)
+# image = Image.open(BytesIO(response.content)).convert("RGB")
+image_path = "path/to/your/image.jpg" # Replace with your image path
+image = Image.open(image_path).convert("RGB")
+
+# Prepare the chat messages in the required multimodal format
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": "Describe this image in detail and answer any questions about it. For example, what is the main subject?"},
+        ],
+    }
+]
+
+# Apply the model's chat template to format the input
+text = processor.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+# Process the inputs (text and image) for the model
+input_ids = processor(text=text, images=image, return_tensors="pt").input_ids.to(model.device)
+
+# Generate the response
+outputs = model.generate(input_ids=input_ids, max_new_tokens=512, do_sample=True, temperature=0.7)
+
+# Decode the generated tokens to a human-readable response
+response = processor.batch_decode(outputs, skip_special_tokens=True)[0]
+
+print(response)
+```
+
+## Data Access
+
+Our two-stage datasets are now available on Hugging Face:
+
+| Stage         | Data                                                                               |
+| :------------ | :--------------------------------------------------------------------------------- |
+| Cold Start    | [Multimodal-Cold-Start](https://huggingface.co/datasets/WaltonFuture/Multimodal-Cold-Start) |
+| RL            | [Multimodal-RL-Data](https://huggingface.co/datasets/WaltonFuture/Multimodal-RL-Data)     |
+
+## Model Access
+
+Our models are now available on Hugging Face:
+
+| Backbone       | Our model                                                     |
+| :------------- | :------------------------------------------------------------ |
+| Qwen2.5-VL-7b  | [Qwen2.5VL-7b-RL-with-Cold-Start](https://huggingface.co/WaltonFuture/Qwen2.5VL-7b-RLCS) |
+| Qwen2.5-VL-3b  | [Qwen2.5VL-3b-RL-with-Cold-Start](https://huggingface.co/WaltonFuture/Qwen2.5VL-3b-RLCS) |
+
+## Acknowledgment
+
+Our models are built upon the amazing [Qwen2.5-VL](https://huggingface.co/collections/Qwen/qwen25-vl-6795ffac22b334a837c0f9a5) family.
+We thank [EasyR1](https://github.com/hiyouga/EasyR1) and [ms-swift](https://github.com/modelscope/ms-swift) for their training codes.
+
+## Citation
+
+If our work has been helpful to you, please consider citing it:
+
+```bibtex
+@article{wei2025advancing,
+  title={Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start},
+  author={Wei, Lai and Li, Yuting and Zheng, Kaipeng and Wang, Chen and Wang, Yue and Kong, Linghe and Sun, Lichao and Huang, Weiran},
+  journal={arXiv preprint arXiv:2505.22334},
+  year={2025}
+}
+```