Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully
  Open MLLMs
Authors: name: Yi Zhang, name: Bolin Ni, name: Xin-Sheng Chen, name: Heng-Rui Zhang, name: Yongming Rao, name: Houwen Peng, name: Qinglin Lu, name: Han Hu, name: Meng-Hao Guo, name: Shi-Min Hu
Organization: Open-Bee

Fully open multimodal large language models (MLLMs) currently lag behind
proprietary counterparts, primarily due to a significant gap in data quality
for supervised fine-tuning (SFT). Existing open-source datasets are often
plagued by widespread noise and a critical deficit in complex reasoning data,
such as Chain-of-Thought (CoT), which hinders the development of advanced model
capabilities. Addressing these challenges, our work makes three primary
contributions. First, we introduce Honey-Data-15M, a new SFT dataset comprising
approximately 15 million QA pairs, processed through multiple cleaning
techniques and enhanced with a novel dual-level (short and long) CoT enrichment
strategy. Second, we introduce HoneyPipe, the data curation pipeline, and its
underlying framework DataStudio, providing the community with a transparent and
adaptable methodology for data curation that moves beyond static dataset
releases. Finally, to validate our dataset and pipeline, we train Bee-8B, an 8B
model on Honey-Data-15M. Experiments show that Bee-8B establishes a new
state-of-the-art (SOTA) for fully open MLLMs, achieving performance that is
competitive with, and in some cases surpasses, recent semi-open models such as
InternVL3.5-8B. Our work delivers to the community a suite of foundational
resources, including: the Honey-Data-15M corpus; the full-stack suite
comprising HoneyPipe and DataStudio; training recipes; an evaluation harness;
and the model weights. This effort demonstrates that a principled focus on data
quality is a key pathway to developing fully open MLLMs that are highly
competitive with their semi-open counterparts.