README.md

---
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: image-text-to-text
tags:
- multi-modal
- large-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!

## Links
Code: https://github.com/LengSicong/MMR1

This model was presented in the paper [LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL](https://arxiv.org/abs/2503.07536). Code can be found at https://github.com/LengSicong/MMR1

## 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}},
}
```