--- title: R-4B canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/R-4B" md_url: "https://www.modelscope.cn/models/AI-ModelScope/R-4B.md" repository: AI-ModelScope/R-4B last_updated: 2025-09-04 license: apache-2.0 pipeline_tag: image-text-to-text tasks: - image-text-to-text model_type: - R architectures: - RForConditionalGeneration base_model: - Qwen/Qwen3-4B base_model_relation: finetune parameters: 4.8B tensor_type: - BF16 library_name: - pytorch - transformer - safetensors frameworks: - pytorch language: - en downloads: 529 stars: 1 --- # R-4B > R-4B - AI-ModelScope 在 ModelScope 开源的模型。R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs via Bi-Mode Annealing and Reinforce Learning AI-ModelScope/R-4B 是 ModelScope 魔搭社区上的 4.8B 参数image-text-to-text模型，采用 apache-2.0 许可，基于 Qwen/Qwen3-4B 构建。 - **Repository**: AI-ModelScope/R-4B - **License**: apache-2.0 - **Tasks**: image-text-to-text - **Parameters**: 4.8B - **Base model**: Qwen/Qwen3-4B - **Downloads**: 529 - **Stars**: 1 - **Last updated**: 2025-09-04 Source: https://www.modelscope.cn/models/AI-ModelScope/R-4B --- # R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs via Bi-Mode Annealing and Reinforce Learning [[📚 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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R-4B Performance
## ⭐️ Introduction 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. The development of R-4B follows a two-stage training paradigm: (1) Bi-mode Annealing, which establishes both thinking and non-thinking capabilities for VQA; and (2) Bi-mode Policy Optimization (BPO), which enables the model to adaptively switch between thinking and non-thinking modes based on input demands. ## 🚀 Key Features - 🧠 **Think Smart, Act Fast: Adaptive & Controllable Thinking!** Our model provides three-mode control over the response process. - **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. - **Support Manual Control:** Explicitly command the model to use its `thinking` or `non-thinking` capabilities, enabling you to make your choices for every job. - 🏆 **Strong Performance, Open for Everyone!** Our model is now **fully open-source**. It achieves **state-of-the-art performance** among models of comparable size. ## 📢 News - **[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. - **[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)! - **[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)! - **[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). ## 🔥 Quickstart Below, we provide simple examples to show how to use R-4B with 🤗 Transformers. ### Using 🤗 Transformers to Chat > [!NOTE] > 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. > Default is `auto-thinking`. ```python import requests from PIL import Image import torch from transformers import AutoModel, AutoProcessor model_path = "YannQi/R-4B" # Load model model = AutoModel.from_pretrained( model_path, torch_dtype=torch.float32, trust_remote_code=True, ).to("cuda") # Load processor processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) # Define conversation messages messages = [ { "role": "user", "content": [ { "type": "image", "image": "http://images.cocodataset.org/val2017/000000039769.jpg", }, {"type": "text", "text": "Describe this image."}, ], } ] # Apply chat template text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, thinking_mode="auto" ) # Load image image_url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(image_url, stream=True).raw) # Process inputs inputs = processor( images=image, text=text, return_tensors="pt" ).to("cuda") # Generate output generated_ids = model.generate(**inputs, max_new_tokens=16384) output_ids = generated_ids[0][len(inputs.input_ids[0]):] # Decode output output_text = processor.decode( output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False ) # Print result print("Auto-Thinking Output:", output_text) ``` ### Using vLLM for fast R-4B deployment and inference. - We recommend using vLLM for fast R-4B deployment and inference. #### Install The code of R-4B requires the newest vllm now. Please install from local source: ```bash git clone https://github.com/vllm-project/vllm.git cd vllm VLLM_USE_PRECOMPILED=1 uv pip install --editable . ``` ##### Online Serving > [!TIP] > The `thinking_mode` switch is also available in APIs created by [vLLM](https://github.com/vllm-project/vllm). > Default is `auto-thinking`. - Serve ```bash vllm serve \ yannqi/R-4B \ --served-model-name r4b \ --tensor-parallel-size 8 \ --gpu-memory-utilization 0.8 \ --host 0.0.0.0 \ --port 8000 \ --trust-remote-code ``` - Openai Chat Completion Client ```python import base64 from PIL import Image from openai import OpenAI # Set OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) # image url image_messages = [ { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": "http://images.cocodataset.org/val2017/000000039769.jpg" }, }, {"type": "text", "text": "Describe this image."}, ], }, ] chat_response = client.chat.completions.create( model="r4b", messages=image_messages, max_tokens=16384, extra_body={ "chat_template_kwargs": {"thinking_mode": "auto"}, }, ) print("Chat response:", chat_response) ``` ## 📈 Experimental Results
R-4B Performance
1. R-4B establishes itself with powerful, state-of-the-art perceptual abilities that are competitive with larger models. 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. ## ✒️ Citation ``` @misc{yang2025r4bincentivizinggeneralpurposeautothinking, title={R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs via Bi-Mode Annealing and Reinforce Learning}, author={Qi Yang and Bolin Ni and Shiming Xiang and Han Hu and Houwen Peng and Jie Jiang}, year={2025}, eprint={2508.21113}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2508.21113}, } ``` ## Acknowledgements 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.