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Model: AI-ModelScope/R-4B Source: Original Platform
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README.md
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README.md
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---
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base_model:
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- Qwen/Qwen3-4B
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language:
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- en
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license: apache-2.0
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pipeline_tag: image-text-to-text
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library_name: transformers
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---
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# R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs via Bi-Mode Annealing and Reinforce Learning
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[[📚 Arxiv Paper](https://arxiv.org/pdf/2508.21113)] [[🤗 Hugging Face](https://huggingface.co/YannQi/R-4B)] [[🤖️ ModelScope](https://huggingface.co/YannQi/R-4B)] [[💻 Code](https://github.com/yannqi/R-4B)]
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<div align="center">
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<img src="asset/logo_R_4B.png" alt="logo" width="38" />
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</div>
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<div align="center">
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<img src="asset/R-4B.png" width="100%" alt="R-4B Performance">
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</div>
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## ⭐️ Introduction
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In this repo, we present **R-4B**, a multimodal large language model designed for general-purpose auto-thinking, autonomously switching between step-by-step thinking and direct response generation based on task complexity. This capability enables R-4B to deliver high-quality responses while significantly improving inference efficiency and reducing computational costs.
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The development of R-4B follows a two-stage training paradigm:
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(1) Bi-mode Annealing, which establishes both thinking and non-thinking capabilities for VQA; and
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(2) Bi-mode Policy Optimization (BPO), which enables the model to adaptively switch between thinking and non-thinking modes based on input demands.
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## 🚀 Key Features
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- 🧠 **Think Smart, Act Fast: Adaptive & Controllable Thinking!**
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Our model provides three-mode control over the response process.
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- **Auto-thinking Mode:** Unleash **auto-thinking** that works across general topics, from simple Q&A to complex scientific analysis. It saves time and computation by thinking only when it matters.
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- **Support Manual Control:** Explicitly command the model to use its `thinking` or `non-thinking` capabilities, enabling you to make your choices for every job.
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- 🏆 **Strong Performance, Open for Everyone!**
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Our model is now **fully open-source**. It achieves **state-of-the-art performance** among models of comparable size.
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## 📢 News
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- **[2025.08.20]** 🚀 **vLLM Support is Here!** Our R-4B model is now fully compatible with [vLLM](https://github.com/vllm-project/vllm) for high-performance inference.
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- **[2025.08.18]** 🏆 **Top Rank Achieved!** We are thrilled to announce that R-4B is now ranked #1 among all open-source models on the [OpenCompass Multi-modal Reasoning Leaderboard](https://rank.opencompass.org.cn/leaderboard-multimodal-reasoning/?m=REALTIME)!
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- **[2025.08.11]** 🥇 **Rank #1!** R-4B ranks first under 20B parameters on the [OpenCompass Multi-modal Academic Leaderboard](https://rank.opencompass.org.cn/leaderboard-multimodal/?m=REALTIME)!
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- **[2025.08.05]** 🎉 **R-4B is Released!** Our model is now publicly available. You can download it from [Hugging Face](https://huggingface.co/YannQi/R-4B).
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## 🔥 Quickstart
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Below, we provide simple examples to show how to use R-4B with 🤗 Transformers.
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### Using 🤗 Transformers to Chat
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> [!NOTE]
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> Users can dynamically control the model's response by selecting one of three modes (`auto-thinking`, `thinking`, or `non-thinking`) with `thinking_mode`. `thinking_mode=auto` for `auto-thinking` mode; `thinking_mode=long` for `thinking` mode; `thinking_mode=short` for `non-thinking` mode.
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> Default is `auto-thinking`.
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```python
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import requests
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from PIL import Image
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import torch
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from transformers import AutoModel, AutoProcessor
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model_path = "YannQi/R-4B"
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# Load model
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model = AutoModel.from_pretrained(
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model_path,
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torch_dtype=torch.float32,
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trust_remote_code=True,
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).to("cuda")
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# Load processor
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processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
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# Define conversation messages
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "http://images.cocodataset.org/val2017/000000039769.jpg",
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},
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{"type": "text", "text": "Describe this image."},
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],
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}
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]
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# Apply chat template
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text = processor.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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thinking_mode="auto"
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)
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# Load image
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image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(image_url, stream=True).raw)
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# Process inputs
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inputs = processor(
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images=image,
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text=text,
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return_tensors="pt"
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).to("cuda")
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# Generate output
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generated_ids = model.generate(**inputs, max_new_tokens=16384)
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output_ids = generated_ids[0][len(inputs.input_ids[0]):]
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# Decode output
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output_text = processor.decode(
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output_ids,
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skip_special_tokens=True,
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clean_up_tokenization_spaces=False
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)
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# Print result
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print("Auto-Thinking Output:", output_text)
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```
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</details>
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### Using vLLM for fast R-4B deployment and inference.
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- We recommend using vLLM for fast R-4B deployment and inference.
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#### Install
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The code of R-4B requires the newest vllm now. Please install from local source:
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```bash
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git clone https://github.com/vllm-project/vllm.git
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cd vllm
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VLLM_USE_PRECOMPILED=1 uv pip install --editable .
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```
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##### Online Serving
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> [!TIP]
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> The `thinking_mode` switch is also available in APIs created by [vLLM](https://github.com/vllm-project/vllm).
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> Default is `auto-thinking`.
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- Serve
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```bash
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vllm serve \
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yannqi/R-4B \
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--served-model-name r4b \
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--tensor-parallel-size 8 \
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--gpu-memory-utilization 0.8 \
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--host 0.0.0.0 \
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--port 8000 \
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--trust-remote-code
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```
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- Openai Chat Completion Client
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```python
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import base64
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from PIL import Image
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from openai import OpenAI
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# Set OpenAI's API key and API base to use vLLM's API server.
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openai_api_key = "EMPTY"
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openai_api_base = "http://localhost:8000/v1"
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client = OpenAI(
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api_key=openai_api_key,
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base_url=openai_api_base,
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)
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# image url
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image_messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": "http://images.cocodataset.org/val2017/000000039769.jpg"
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},
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},
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{"type": "text", "text": "Describe this image."},
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],
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},
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]
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chat_response = client.chat.completions.create(
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model="r4b",
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messages=image_messages,
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max_tokens=16384,
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extra_body={
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"chat_template_kwargs": {"thinking_mode": "auto"},
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},
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)
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print("Chat response:", chat_response)
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```
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## 📈 Experimental Results
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<div align="center">
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<img src="asset/performance.png" width="100%" alt="R-4B Performance">
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</div>
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1. R-4B establishes itself with powerful, state-of-the-art perceptual abilities that are competitive with larger models.
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2. In evaluation sets that require complex logical reasoning and mathematical problem-solving, such as WeMath, MathVerse, and LogicVista, R-4B displays a strong performance curve. This highlights its advanced adaptive thinking capacity for logical deduction and solving complex quantitative problems.
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## ✒️ Citation
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```
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@misc{yang2025r4bincentivizinggeneralpurposeautothinking,
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title={R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs via Bi-Mode Annealing and Reinforce Learning},
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author={Qi Yang and Bolin Ni and Shiming Xiang and Han Hu and Houwen Peng and Jie Jiang},
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year={2025},
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eprint={2508.21113},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2508.21113},
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}
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```
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## Acknowledgements
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R-4B is developed based on the codebases of the following projects: [LLaVA-Next](https://github.com/LLaVA-VL/LLaVA-NeXT), [SigLIP2](https://huggingface.co/google/siglip2-so400m-patch14-384), [Qwen3](https://github.com/QwenLM/Qwen3), [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL), [VLMEvalKit](https://github.com/open-compass/VLMEvalKit). We sincerely thank these projects for their outstanding work.
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