--- title: Bee-8B-RL canonical_url: "https://www.modelscope.cn/models/Open-Bee/Bee-8B-RL" md_url: "https://www.modelscope.cn/models/Open-Bee/Bee-8B-RL.md" repository: Open-Bee/Bee-8B-RL last_updated: 2026-03-10 license: apache-2.0 pipeline_tag: image-text-to-text tasks: - image-text-to-text model_type: - Bee architectures: - BeeForConditionalGeneration base_model: - Qwen/Qwen3-8B base_model_relation: finetune parameters: 2.1B tensor_type: - BF16 library_name: - pytorch - transformer - safetensors frameworks: - pytorch language: - en downloads: 224 stars: 1 tags: - Bee-8B - Fully-Open-MLLMs --- # Bee-8B-RL > Bee-8B-RL - Open-Bee 在 ModelScope 开源的模型。Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully Open MLLMs Open-Bee/Bee-8B-RL 是 ModelScope 魔搭社区上的 2.1B 参数image-text-to-text模型，采用 apache-2.0 许可，基于 Qwen/Qwen3-8B 构建。 - **Repository**: Open-Bee/Bee-8B-RL - **License**: apache-2.0 - **Tasks**: image-text-to-text - **Parameters**: 2.1B - **Base model**: Qwen/Qwen3-8B - **Tags**: Bee-8B, Fully-Open-MLLMs - **Downloads**: 224 - **Stars**: 1 - **Last updated**: 2026-03-10 Source: https://www.modelscope.cn/models/Open-Bee/Bee-8B-RL --- # Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully Open MLLMs [[🏠 Homepage](https://open-bee.github.io/)] [[📖 Arxiv Paper](https://arxiv.org/pdf/2510.13795)] [[🤗 Models & Datasets](https://huggingface.co/collections/Open-Bee/bee-8b-68ecbf10417810d90fbd9995)] [[💻 Code](https://github.com/Open-Bee)] ## Introduction We introduce **Bee-8B**, a new state-of-the-art, fully open 8B Multimodal Large Language Model (MLLM) designed to close the performance gap with proprietary models by focusing on data quality. Bee-8B is trained on our new **Honey-Data-15M** corpus, a high-quality supervised fine-tuning (SFT) dataset of approximately 15 million samples. This dataset was meticulously created with our transparent, adaptable, and open-source data curation pipeline, **HoneyPipe**, which systematically cleans noisy data and enriches it with a novel dual-level (short and long) Chain-of-Thought (CoT) strategy. This dataset enables Bee-8B to achieve exceptional performance, particularly in complex reasoning, establishing a new standard for fully open MLLMs. ## Key Features - **High-Quality, Large-Scale Dataset:** We release **Honey-Data-15M**, a new 15M-sample SFT corpus. It has undergone extensive cleaning to remove widespread noise and has been enriched with dual-level CoT reasoning to enhance advanced problem-solving capabilities. - **Fully Open-Source Data Curation Suite:** We provide not just the data, but the entire methodology. **HoneyPipe** and its underlying framework **DataStudio** offer the community a transparent and reproducible pipeline, moving beyond static dataset releases. - **State-of-the-Art Open Model:** Our model, **Bee-8B**, achieves state-of-the-art performance among fully open MLLMs and is highly competitive with recent semi-open models like InternVL3.5-8B, demonstrating the power of high-quality data. ## News - **[2026.03.08]** 💻 We've open-sourced our main code! Since our training code relies on a somewhat outdated [LLaVA-Next](https://github.com/LLaVA-VL/LLaVA-NeXT) codebase, it requires significant cleanup. We will organize and release it when we have time. We're also planning to submit PRs to popular repos like Llama-Factory and MS-Swift soon. - **[2026.01.26]** 🎉 Our paper has been accepted to ICLR 2026!. All the code will be open source soon. - **[2025.12.17]** 🔥 We have released all data and model weights across different stages. For the final stage (RL data), you can directly merge [ViRL39K](https://huggingface.co/datasets/TIGER-Lab/ViRL39K) and [MMK12](https://huggingface.co/datasets/FanqingM/MMK12) and use the [VeRL](https://github.com/volcengine/verl) framework for training. - **[2025.11.03]** 📊 **[Honey-Data-15M](https://huggingface.co/datasets/Open-Bee/Honey-Data-15M) & [Honey-Data-1M](https://huggingface.co/datasets/Open-Bee/Honey-Data-1M) is Released\!** You can download the 15M full version and the 1M efficient version from [HuggingFace]((https://huggingface.co/collections/Open-Bee/bee-8b-68ecbf10417810d90fbd9995)). - **[2025.10.20]** 🚀 **vLLM Support is Here!** Bee-8B now supports high-performance inference with [vLLM](https://github.com/vllm-project/vllm), enabling faster and more efficient deployment for production use cases. - **[2025.10.13]** 🐝 **Bee-8B is Released\!** Our model is now publicly available. You can download it from [Hugging Face](https://huggingface.co/collections/Open-Bee/bee-8b-68ecbf10417810d90fbd9995). ## Quickstart > [!NOTE] > Below, we provide simple examples to show how to use Bee-8B with 🤗 Transformers. > You can dynamically control the model's response by selecting one of two modes: set `enable_thinking=True` for `thinking` mode, or `enable_thinking=False` for `non-thinking` mode. The default is `thinking` mode. ### Using 🤗 Transformers to Chat ```python import requests import torch from PIL import Image from transformers import AutoModel, AutoProcessor model_path = "Open-Bee/Bee-8B-RL" # Load model model = AutoModel.from_pretrained( model_path, torch_dtype=torch.bfloat16, 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": "https://huggingface.co/Open-Bee/Bee-8B-RL/resolve/main/assets/logo.png", }, { "type": "text", "text": "Based on this picture, write an advertising slogan about Bee-8B (a Fully Open Multimodal Large Language Model)." }, ], }] # Apply chat template text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True) # Load image image_url = "https://huggingface.co/Open-Bee/Bee-8B-RL/resolve/main/assets/logo.png" 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, temperature=0.6) output_ids = generated_ids[0][len(inputs.input_ids[0]):] # Decode output output_text = processor.decode(output_ids, skip_special_tokens=True) # Print result print(output_text) ``` ### Using vLLM for High-Performance Inference #### Install vLLM > [!IMPORTANT] > Bee-8B support will be officially available in vLLM **v0.11.1**. Until then, please install vLLM from source: ```bash git clone https://github.com/vllm-project/vllm.git cd vllm VLLM_USE_PRECOMPILED=1 uv pip install --editable . ``` Once vLLM v0.11.1 is released, you will be able to install it directly via pip: ```bash pip install vllm>=0.11.1 ``` #### Offline Inference ```python from transformers import AutoProcessor from vllm import LLM, SamplingParams from PIL import Image import requests def main(): model_path = "Open-Bee/Bee-8B-RL" llm = LLM( model=model_path, limit_mm_per_prompt={"image": 5}, trust_remote_code=True, tensor_parallel_size=1, gpu_memory_utilization=0.8, ) sampling_params = SamplingParams( temperature=0.6, max_tokens=16384, ) image_url = "https://huggingface.co/Open-Bee/Bee-8B-RL/resolve/main/assets/logo.png" image = Image.open(requests.get(image_url, stream=True).raw) messages = [ { "role": "user", "content": [ { "type": "image", "image": image }, { "type": "text", "text": "Based on this picture, write an advertising slogan about Bee-8B (a Fully Open Multimodal Large Language Model)." }, ], }, ] processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) prompt = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True, ) mm_data = {"image": image} llm_inputs = { "prompt": prompt, "multi_modal_data": mm_data, } outputs = llm.generate([llm_inputs], sampling_params=sampling_params) generated_text = outputs[0].outputs[0].text print(generated_text) if __name__ == '__main__': main() ``` #### Online Serving - Start the server ```bash vllm serve \ Open-Bee/Bee-8B-RL \ --served-model-name bee-8b-rl \ --tensor-parallel-size 8 \ --gpu-memory-utilization 0.8 \ --host 0.0.0.0 \ --port 8000 \ --trust-remote-code ``` - Using OpenAI Python Client to Query the server ```python 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": "https://huggingface.co/Open-Bee/Bee-8B-RL/resolve/main/assets/logo.png" }, }, { "type": "text", "text": "Based on this picture, write an advertising slogan about Bee-8B (a Fully Open Multimodal Large Language Model)." }, ], }, ] chat_response = client.chat.completions.create( model="bee-8b-rl", messages=image_messages, max_tokens=16384, extra_body={ "chat_template_kwargs": { "enable_thinking": True }, }, ) print("Chat response:", chat_response.choices[0].message.content) ``` ## Experimental Results
logo
Evaluation of Bee-8B against other MLLMs. We distinguish between fully open (*) and semi-open (†) models. The top and second-best scores for each benchmark are highlighted.
1. **New State-of-the-Art:** Bee-8B establishes a new performance standard for fully open MLLMs, proving highly competitive with recent semi-open models across a wide array of benchmarks. 2. **Excellence in Complex Reasoning:** Thanks to the CoT-enriched Honey-Data-15M, Bee-8B shows its most significant advancements in complex math and reasoning. It achieves top scores on challenging benchmarks like **MathVerse**, **LogicVista**, and **DynaMath**. 3. **Superior Document and Chart Understanding:** The model demonstrates powerful capabilities in analyzing structured visual data, securing the top rank on the **CharXiv** benchmark for both descriptive and reasoning questions. ## Acknowledgements Bee-8B is developed based on the architectures and codebases of the following projects: [R-4B](https://huggingface.co/YannQi/R-4B), [LLaVA-OneVision](https://github.com/LLaVA-VL/LLaVA-NeXT), [SigLIP2](https://huggingface.co/google/siglip2-so400m-patch14-384), [Qwen3](https://github.com/QwenLM/Qwen3), and evaluated using [VLMEvalKit](https://github.com/open-compass/VLMEvalKit). We sincerely thank these projects for their outstanding contributions to the open-source community.