---
title: Honey-Data-15M
canonical_url: "https://www.modelscope.cn/datasets/Open-Bee/Honey-Data-15M"
md_url: "https://www.modelscope.cn/datasets/Open-Bee/Honey-Data-15M.md"
repository: Open-Bee/Honey-Data-15M
last_updated: 2026-10-17
license: apache-2.0
storage_size: "4.3 TB"
downloads: 32676
stars: 16
---

# Honey-Data-15M

> Honey-Data-15M - Open-Bee 在 ModelScope 开源的数据集。Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully Open MLLMs

Open-Bee/Honey-Data-15M 是 ModelScope 魔搭社区上的数据集，存储大小 4.3 TB，采用 apache-2.0 许可。

- **Repository**: Open-Bee/Honey-Data-15M
- **License**: apache-2.0
- **Storage size**: 4.3 TB
- **Downloads**: 32676
- **Stars**: 16
- **Last updated**: 2026-10-17

Source: https://www.modelscope.cn/datasets/Open-Bee/Honey-Data-15M

---

# 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)]

> [!TIP]
> **A newer, larger version is available: [Honey-Data-V2](https://huggingface.co/datasets/HoneyDataV2/Honey-Data-V2).**
> Released with the journal version of Bee (*Computational Visual Media*), it reapplies HoneyPipe to this corpus at a
> larger scale: the Honey-Data-15M pool is re-curated to fix community-reported defects (e.g. samples whose final turn
> has no answer, grounding instructions with an implicit coordinate convention), re-annotated by an upgraded model stack
> with up to three verified answers per instruction, and extended with newly released community corpora, reaching
> **44.3M samples comprising 100.3M QA pairs**. Honey-Data-15M remains the corpus the released Bee-8B checkpoints were
> trained on.

## 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.

## Honey-Data-15M
> [!NOTE]
> The dataset's responses adhere to two specific tag structures: Short CoT responses are formatted as `<think>\n\n</think>\n\n{short CoT Response}`, while Long CoT responses follow the format `<think>\n{Long CoT Reasoning}\n</think>\n\n`. More details about the dataset can be found in the [Paper](https://arxiv.org/abs/2510.13795).

> [!NOTE]
> The complete data is 4.71 T and has been completely transmitted. Due to a bug in the dataviewer, the size and number of items displayed by huggingface are inaccurate.

Honey-Data-15M is a large-scale, high-quality supervised fine-tuning (SFT) dataset containing approximately **15 million** meticulously curated samples. We built this dataset with the core objective of addressing the quality bottleneck in current open-source data by systematically cleaning widespread data noise and enriching the data with an innovative **"Dual-Level Chain-of-Thought (CoT)"** strategy.

The dataset's composition is as follows:

* **Approximately 12.2 million short CoT samples**: Designed to instill foundational, step-by-step logical inference in the model.
* **Approximately 2.7 million long CoT samples**: Focused on more intricate, multi-step reasoning problems that challenge and enhance the model's advanced cognitive abilities.

## Usage

To load the dataset, you can refer to the following code:
```python
from PIL import Image
from datasets import load_dataset

# Load dataset (using CoSyn_Math subset as example)
item = load_dataset("Open-Bee/Honey-Data-15M",
                    split="train",
                    name="CoSyn_Math")[0]

# Extract data fields
item_id = item['id']
conversations = item['conversations']
images_data = item.get('images', [])
source = item.get('source', None)
img_phash = item.get('img_phash', None)
img_size = item.get('img_size', None)

# Save images and record paths
image_paths = []
for img_idx, image_data in enumerate(images_data):
    image_filename = f"{item_id}_{img_idx}.jpg"
    image_path = image_filename

    # Save image (datasets automatically converts to PIL Image object)
    if isinstance(image_data, Image.Image):
        # JPEG format requires RGB mode
        if image_data.mode in ('RGBA', 'LA', 'P'):
            image_data = image_data.convert('RGB')
        image_data.save(image_path, format='JPEG')
        image_paths.append(image_path)

# Build sample
sample = {
    'id': item_id,
    'conversations': conversations,
    'image': image_paths[0] if len(image_paths) == 1 else image_paths,
    'source': source,
    'img_phash': img_phash,
    'img_size': img_size,
}

# Print result
print(sample)
```

## Licensing Information
The `Honey-Data-15M` dataset is a collection composed of multiple publicly available sub-datasets. Each of these sub-datasets is governed by its own original license.

- **Sub-dataset Licenses:** Users of `Honey-Data-15M` must strictly adhere to the specific licensing terms and conditions of each original sub-dataset included in this collection. We recommend you carefully review the original license for each sub-dataset before use.

- **Prompts and Responses:** To the extent that we hold any intellectual property rights in the modified prompts and newly generated responses created for this project, these contributions are made available under the **Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC-4.0)** license.

- **Copyright Concerns:** This dataset is compiled for academic research purposes. If you believe any content within `Honey-Data-15M` infringes upon your copyright, please contact us immediately at yi.zhang.4096[at]gmail.com. We will promptly review and address the matter, including the removal of concerned content upon verification.

## Acknowledgements

> [!NOTE]
> If you believe we have missed acknowledging any important data source that should be explicitly mentioned here, please contact us.

Honey-Data-15M is built upon a large collection of publicly available datasets. We extend our deepest gratitude to the creators and maintainers of the following major datasets.

- [LLaVA-OneVision-Data](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data): A comprehensive multimodal instruction tuning dataset
- [MAmmoTH-VL-Instruct-12M](https://huggingface.co/datasets/MAmmoTH-VL/MAmmoTH-VL-Instruct-12M): A large-scale vision-language instruction dataset for mathematical reasoning
- [VisualWebInstruct](https://huggingface.co/datasets/TIGER-Lab/VisualWebInstruct): A dataset for web-based visual instruction following
- [ArXiv-OCR-v0.2](https://huggingface.co/datasets/nz/arxiv-ocr-v0.2): OCR data from ArXiv papers for document understanding
- [CoSyn-400K](https://huggingface.co/datasets/allenai/CoSyn-400K): Synthetic data for visual reasoning across multiple domains
- [PixMo Collection](https://huggingface.co/collections/allenai/pixmo): A collection of high-quality vision-language datasets
- And many other datasets including [Cauldron](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron), [Cambrian](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M), and numerous individual datasets across VQA, OCR, Charts, STEM, and other domains.

## Citation
If you use our dataset in your research, please cite our paper:
```bibtex
@inproceedings{zhang2026bee,
  title     = {Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully Open MLLMs},
  author    = {Yi Zhang and Bolin Ni and Xin-Sheng Chen and Heng-Rui Zhang and Yongming Rao and
               Houwen Peng and Qinglin Lu and Han Hu and Meng-Hao Guo and Shi-Min Hu},
  booktitle = {The Fourteenth International Conference on Learning Representations},
  year      = {2026},
}
```

The journal version, which extends this work to a unified SFT + RL recipe and releases
[Honey-Data-V2](https://huggingface.co/datasets/HoneyDataV2/Honey-Data-V2):

```bibtex
@article{zhang2026beecvm,
  title     = {Bee: A High-Quality Corpus and Full-Stack Suite for End-to-End Training of Fully Open MLLMs},
  author    = {Yi Zhang and Bolin Ni and Xin-Sheng Chen and Heng-Rui Zhang and Yongming Rao and
               Houwen Peng and Qinglin Lu and Han Hu and Meng-Hao Guo and Shi-Min Hu},
  journal   = {Computational Visual Media},
  year      = {2026},
  note      = {to appear},
}
```