What is Bee
Bee is a high-quality multimodal large language model (MLLM) project jointly launched by Tsinghua University and the Tencent Hunyuan team, addressing the performance bottlenecks of open-source models caused by insufficient data quality. The project's core contributions include: Honey-Data-15M, a high-quality supervised fine-tuning dataset containing approximately 15 million question-answer pairs, with data quality improved through multi-step cleaning and a dual-layer chain-of-thought (CoT) expansion strategy; HoneyPipe and DataStudio, open-source data curation pipelines and frameworks that provide transparent and reproducible data processing methods; and the Bee-8B model, an 8B-parameter model trained on Honey-Data-15M, which sets new SOTA records for fully open-source MLLMs in multiple benchmarks, with performance comparable to or even better than some semi-open-source models.
Bee's Main Features
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High-Quality Dataset Construction: Releases Honey-Data-15M, a 15-million-scale supervised fine-tuning dataset that has undergone fine cleaning and dual-layer chain-of-thought (CoT) expansion, significantly improving data quality and providing a solid foundation for multimodal large model training.
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Full-Stack Data Processing Pipeline: Open-sources HoneyPipe and DataStudio, providing a full-process data processing method from data aggregation and noise filtering to CoT enhancement, ensuring transparency and reproducibility in data curation and surpassing traditional static dataset release models.
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High-Performance Model Training and Validation: Trains the Bee-8B model based on Honey-Data-15M, setting performance records for fully open-source multimodal large language models in multiple benchmarks and proving the key role of high-quality data in enhancing model capabilities.
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Open-Source Ecosystem Construction: Provides complete open-source resources, including datasets, data processing pipelines, training recipes, evaluation tools, and model weights, promoting the development of the open-source community and empowering academia and developers in research and applications in the field of multimodal large models.
Bee's Technical Principles
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Data Aggregation and Deduplication: Collects large numbers of image-text pairs from multiple data sources and ensures data diversity and efficient processing through strict deduplication.
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Noise Filtering: Uses a combination of rules and models to remove noisy data such as format errors, low-quality images, or instruction mismatches, improving data quality.
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Chain-of-Thought (CoT) Expansion: Uses two strategies, short CoT and long CoT, to generate detailed reasoning processes for instructions of different complexity, enhancing the model's reasoning ability.
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Fidelity Verification: Uses a verification model (LLM-as-a-Judge) for semantic comparison to ensure the correctness and consistency of the generated CoT responses.
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Model Training and Optimization: Trains the Bee-8B model based on the high-quality dataset Honey-Data-15M, and optimizes model performance through techniques such as supervised fine-tuning (SFT) and reinforcement learning (RL).
Bee's Project Address
- Project Website：https://open-bee.github.io/
- HuggingFace Model Repository：https://huggingface.co/collections/Open-Bee/bee
- arXiv Technical Paper：https://arxiv.org/pdf/2510.13795
- Honey-Data-15M Dataset：https://huggingface.co/datasets/Open-Bee/Honey-Data-15M
Bee's Application Scenarios
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Multimodal Content Generation: Used to generate high-quality image descriptions, video subtitles, and more, improving the efficiency and diversity of content creation.
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Intelligent Question Answering Systems: In answering complex questions, uses its powerful reasoning ability to provide users with accurate and detailed answers.
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Education Sector: Assist teaching, generate teaching materials or answer student questions, and support personalized learning.
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Research Assistance: Help researchers organize and analyze data, and generate research reports or experimental design suggestions.
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Business Intelligence: Analyze market trends, user feedback, etc., to provide data support and predictions for decision-making.
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Healthcare: Assist medical diagnosis, generate medical image analysis reports, or provide medical consultation advice.