Create README.md

README.md

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+---
+library_name: transformers
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
+pipeline_tag: visual-question-answering
+tags:
+- multi-modal
+- large-language-model
+- video-language-model
+---
+
+<p align="center">
+    <img src="https://github.com/LengSicong/MMR1/blob/main/assets/logo.png?raw=true" width="150" style="margin-bottom: 0.2;"/>
+<p>
+
+<h3 align="center">
+MMR1: Advancing the Frontiers of Multimodal Reasoning</a></h3>
+<h5 align="center"> If you like our project, please give us a star ⭐ on <a href="https://github.com/LengSicong/MMR1">Github</a> to support us. 🙏🙏 </h2>
+
+## 📰 News
+* **[2025.03.11]**  🔥🔥 Release MMR1-Math-v0, achieving SOTA with only 6k data!
+
+
+## Model Description
+MMR1-Math-v0-7B is a Large Multimodal Model specialized in mathematical tasks. Remarkably, MMR1-Math-v0-7B achieves state-of-the-art performance among open-source 7B multimodal models, competing effectively even against proprietary models with significantly larger parameter sizes—all trained using only 6k carefully curated data instances.
+
+### Key Highlights:
+
+- **SOTA Performance**: Sets a new **state-of-the-art** benchmark on math-related multimodal tasks among open-source 7B models.
+
+- **Minimal Training Data**: Remarkably achieves top-tier performance with just **6k** high-quality samples from **public training datasets**.
+
+- **Efficient Training with GRPO**: 6 hours of RL training with 64 H100s for 15 epochs.
+
+- **Public and High-Quality Data**: Publicly sourced datasets, rigorously filtered and balanced across both difficulty and mathematical problem types.
+
+- **Balanced Data Strategy**: Uniform sampling of data based on both task difficulty (filtering out overly simple problems) and mathematical reasoning diversity.
+
+
+## Evaluation Results
+
+We evaluated our model using [VLMEvalKit](https://github.com/open-compass/VLMEvalKit/tree/main) on four mathematical reasoning benchmarks: MathVista_MINI, MathVision, LogicVista, and MathVerse_MINI.
+
+We also include results on the MathVerse_MINI_Vision_Only_cot (MathVerse_V) subset to maintain consistency with the VLMEvalKit leaderboard. The table below compares our model's performance against various open-source and proprietary models.
+
+| Model | size | MathVista | MathVision | LogicVista | MathVerse | MathVerse_V |
+|-------|:----:|:--------------:|:----------:|:----------:|:--------------:|:-------------------:|
+| **Close-sourced** | | | | | | |
+| [GPT-4o 1120](https://openai.com/index/gpt-4o-system-card/)  | - | 60.0 | 31.2 | 52.8 | 40.6 | - |
+| [Gemini-2.0-flash](https://deepmind.google/technologies/gemini/flash/) | - | 70.4 | 43.6 | 52.3 | 47.8 | - |
+| [Claude3.7-Sonnet](https://www.anthropic.com/news/claude-3-7-sonnet) | - | 66.8 | 41.9 | 58.2 | 46.7 | - |
+| **R1-related** | | | | | | |
+| [LLaVA-CoT](https://github.com/PKU-YuanGroup/LLaVA-CoT) | 11B | 52.5 | 19.9 | 39.6 | 22.6 | - |
+| [Open-R1-Multimodal](https://github.com/EvolvingLMMs-Lab/open-r1-multimodal) | 7B | 60.6 | - | - | - | - |
+| [Mulberry](https://github.com/HJYao00/Mulberry) | 7B | 63.1 | - | - | - | - |
+| [LMM-R1](https://arxiv.org/abs/2503.07536) | 3B | 63.2 | 26.4 | - | - | 41.6 |
+| [R1-Onevision](https://github.com/Fancy-MLLM/R1-Onevision?tab=readme-ov-file) | 7B | - | 26.2 | - | - | 44.1 |
+| [MM-Eureka](https://github.com/ModalMinds/MM-EUREKA) | 8B | 67.1 | 22.2 | - | - | 40.4 |
+| [MM-Eureka](https://github.com/ModalMinds/MM-EUREKA) | 38B | 64.2 | 26.6 | - | - | 48.9 |
+| **Open-sourced** | | | | | | |
+| [Ovis2-8b](https://github.com/AIDC-AI/Ovis) | 8B | 71.8 | 25.9 | 39.4 | 42.3 | - |
+| [MiniCPM-o-2.6](https://github.com/OpenBMB/MiniCPM-o) | 8B | **71.9** | 21.7 | 36.0 | 35.0 | - |
+| [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL) (official) | 7B | 68.2 | 25.4 | 47.9 | 41.1 | - |
+| [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL) (reproduced) | 7B | 67.5 | 25.6 | 46.8 | 42.5 | 46.9 |
+| **Ours** | | | | | | |
+| **MMR1-math-v0** | 7B | 71.0 | **30.2** | **50.8** | **45.1** | **49.8** |
+
+
+
+### Quick Start
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+# default: Load the model on the available device(s)
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "MMR1/MMR1-Math-v0-7B", 
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    device_map="auto",
+)
+# default processer
+processor = AutoProcessor.from_pretrained("MMR1/MMR1-Math-v0-7B")
+# Example input
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "path/to/image.jpeg",
+            },
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+<details>
+<summary>Batch inference</summary>
+
+```python
+# Sample messages for batch inference
+messages1 = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "file:///path/to/image1.jpg"},
+            {"type": "image", "image": "file:///path/to/image2.jpg"},
+            {"type": "text", "text": "What are the common elements in these pictures?"},
+        ],
+    }
+]
+messages2 = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Who are you?"},
+]
+# Combine messages for batch processing
+messages = [messages1, messages2]
+# Preparation for batch inference
+texts = [
+    processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True)
+    for msg in messages
+]
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=texts,
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+# Batch Inference
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_texts = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_texts)
+```
+</details>
+
+
+## Citation
+
+If you find MMR1 useful for your research and applications, please cite using this BibTeX:
+
+```bibtex
+@misc{MMR1-Math2025,
+  title={MMR1: Advancing the Frontiers of Multimodal Reasoning},
+  author={Sicong Leng*, Jing Wang*, Jiaxi Li*, Hao Zhang*, Zhiqiang Hu, Boqiang Zhang, Hang Zhang, Yuming Jiang, Xin Li, Fan Wang, Yu Rong, Aixin Sun†, Shijian Lu†},
+  year={2025},
+  howpublished={\url{https://github.com/LengSicong/MMR1}},
+}
+```