---
title: HoneyDataV2-Honey-Data-V2
canonical_url: "https://www.modelscope.cn/datasets/hf/HoneyDataV2-Honey-Data-V2"
md_url: "https://www.modelscope.cn/datasets/hf/HoneyDataV2-Honey-Data-V2.md"
repository: hf/HoneyDataV2-Honey-Data-V2
last_updated: 2026-10-14
license: other
storage_size: "5.3 TB"
downloads: 2642
stars: 0
---

# HoneyDataV2-Honey-Data-V2

> HoneyDataV2-Honey-Data-V2 - hf 在 ModelScope 开源的数据集。[🏠 Homepage] [📖 arXiv (conference version)] [🤗 Bee models & datasets] [🍯 Honey-Data-15M] [💻 Code]

hf/HoneyDataV2-Honey-Data-V2 是 ModelScope 魔搭社区上的数据集，存储大小 5.3 TB，采用 other 许可。

- **Repository**: hf/HoneyDataV2-Honey-Data-V2
- **License**: other
- **Storage size**: 5.3 TB
- **Downloads**: 2642
- **Stars**: 0
- **Last updated**: 2026-10-14

Source: https://www.modelscope.cn/datasets/hf/HoneyDataV2-Honey-Data-V2

---

# Honey-Data-V2

[[🏠 Homepage](https://open-bee.github.io/)] [[📖 arXiv (conference version)](https://arxiv.org/abs/2510.13795)] [[🤗 Bee models & datasets](https://huggingface.co/collections/Open-Bee/bee-8b-68ecbf10417810d90fbd9995)] [[🍯 Honey-Data-15M](https://huggingface.co/datasets/Open-Bee/Honey-Data-15M)] [[💻 Code](https://github.com/orgs/Open-Bee/repositories)]

**Honey-Data-V2 is the corpus of the journal version of Bee**, *Bee: A High-Quality Corpus and Full-Stack
Suite for End-to-End Training of Fully Open MLLMs*, accepted to *Computational Visual Media*. The
conference version (ICLR 2026) released [Honey-Data-15M](https://huggingface.co/datasets/Open-Bee/Honey-Data-15M)
and the [Bee-8B](https://huggingface.co/collections/Open-Bee/bee-8b-68ecbf10417810d90fbd9995) models trained
on it. The journal version scales the corpus up by reapplying the same curation pipeline, HoneyPipe, at a
substantially larger scale.

Honey-Data-V2 is a multimodal supervised fine-tuning corpus of **44.3M samples comprising 100.3M QA pairs**.
It is stored as **19,707,852 image groups** carrying **44,295,078 conversations**, spread over 8 task
categories and 420 subsets (5.81 TB). Long chain-of-thought covers 17.9% of the samples (7.9M, against 2.9M
in Honey-Data-15M).

> The released Bee-8B checkpoints, and all results reported in the paper, were trained on Honey-Data-15M.
> Evaluating models trained on Honey-Data-V2 is left for future work.

## What is new compared with Honey-Data-15M

Honey-Data-V2 is not built from scratch. It extends Honey-Data-15M in three ways, all executed with the
existing operators of DataStudio:

- **Re-curation of the existing pool.** Acting on community defect reports, samples whose final turn
  carries no answer were removed, and 492.6K grounding instructions were rewritten to state their coordinate
  convention up front. 1.8M machine-recaptioning samples were dropped, because they dominated by volume while
  adding little instruction-following signal. Image placeholders were canonicalized, and degenerate generations
  (unpaired reasoning tags, repetition collapse, placeholder leakage) were rejected.
- **Multi-model rollout.** On top of the original annotation pass, two further independent rollouts answered
  the same instructions with an upgraded model stack. Every verified answer is kept, so an instruction carries
  one, two or three independently produced answers. Over the overlapping subset, two answers to the same
  instruction agree verbatim in only 0.00%–0.89% of cases.
- **New source incorporation.** The [FineVision](https://huggingface.co/datasets/HuggingFaceM4/FineVision)
  datasets absent from Honey-Data-15M, together with Molmo2-SynMultiImageQA, ChartVerse and Aria-UI, were passed
  through the same pipeline rather than concatenated as-is. Sources whose answers are already determinate, such
  as OCR transcription and box-level grounding, were filtered but not rewritten.

| lineage | samples | long CoT |
|---|---:|---:|
| re-curated Honey-Data-15M pool (rollout passes 1 / 2 / 3: 9.6M / 12.8M / 7.0M) | 29.4M | 15.5% |
| newly incorporated sources | 14.9M | 22.6% |
| **Honey-Data-V2 total** | **44.3M** | **17.9%** |

| role | Honey-Data-15M | Honey-Data-V2 |
|---|---|---|
| short CoT | Qwen2.5-VL-72B/32B | Qwen3-VL-235B-A22B-Instruct, Kimi-K2.5 |
| long CoT | proprietary MLLMs | Qwen3-VL-235B-A22B-Thinking, Doubao-Thinking, Kimi-K2.5 |
| verification | Qwen2.5-VL-72B | Qwen3-235B-A22B-Instruct |
| GUI grounding | – | MAI-UI-8B |

## One record is one image group

Unlike the usual one-row-per-conversation layout, a record here collects **every conversation that
refers to the same image**. The image bytes are therefore stored once, no matter how many dialogues
discuss it. Across the corpus this is the difference between 19,708,047 stored images and the
44,295,078 conversations that use them.

```python
from datasets import load_dataset, get_dataset_config_names

subsets = get_dataset_config_names("HoneyDataV2/Honey-Data-V2")
ds = load_dataset("HoneyDataV2/Honey-Data-V2", subsets[0], split="train")

rec = ds[0]
rec["images"][0]                    # a PIL image
len(rec["data"])                    # how many dialogues share it
rec["data"][0]["messages"]          # [{'from': 'human', 'value': ...}, {'from': 'gpt', 'value': ...}]
rec["data"][0]["provenance"]        # which model wrote and which verified this answer
```

## Image encoding

Images are stored as **lossless WebP**. Where the source was already JPEG, or
where WebP came out larger than the original, the original bytes are kept
untouched. WebP holds the same pixels as the PNGs it replaces in roughly 60% of
the bytes. Decoding is transparent: `datasets` returns PIL images either way.

For RGBA images the alpha channel is preserved exactly and RGB is preserved
wherever alpha > 0. PNG stores arbitrary colour under fully transparent pixels
and WebP does not carry it over; no reader can observe those values.

### Record fields

| column | type | meaning |
|---|---|---|
| `id` | string | identifier of the image group |
| `images` | list of image | the images, decoded to PIL by `datasets` |
| `n_images` | int32 | how many images the group holds |
| `image_sizes` | list of [width, height] | pixel dimensions, one pair per image |
| `image_phashes` | list of string | perceptual hash, one per image |
| `category` | string | task category; matches the directory |
| `source_subset` | string | subset; matches the directory |
| `categories` | list of string | every category label the upstream corpora gave this group |
| `sources` | list of string | every upstream corpus that contributed a dialogue |
| `source_subsets` | list of string | every upstream subset name |
| `data` | list of struct | the dialogues, below |

Each element of `data`:

| field | meaning |
|---|---|
| `id`, `ori_id` | identifiers of this dialogue and of the record it came from |
| `messages` | the dialogue as `from` / `value` pairs, `human` and `gpt` |
| `n_turns` | number of turns |
| `cot_level` | `short` or `long` chain-of-thought |
| `original_answers` | the answers before rewriting |
| `provenance` | which model performed each step |
| `source`, `subset`, `source_subset`, `category` | where this dialogue came from |

`provenance` names the model used at each stage, so a subset of the corpus can be selected by
annotator: `question_image_consist` (the image-question consistency check), `answer_rewrite`,
`reasoning_rewrite`, and `answer_consist` (verification of the rewritten answer). A value of `null`
means that stage did not apply — determinate tasks such as OCR transcription and box-level grounding
are filtered but never rewritten.

## Composition

| category | subsets | image groups | conversations | size |
|---|---:|---:|---:|---:|
| Caption | 13 | 1,413,122 | 2,011,253 | 1.21 TB |
| Chart | 77 | 4,665,363 | 11,036,064 | 0.87 TB |
| Document | 49 | 2,271,245 | 4,766,060 | 0.67 TB |
| GUI-Grounding | 4 | 500,916 | 2,803,848 | 0.12 TB |
| General | 122 | 4,298,102 | 12,952,088 | 1.75 TB |
| Grounding | 27 | 1,979,196 | 3,435,355 | 0.75 TB |
| OCR | 45 | 2,564,793 | 3,625,483 | 0.37 TB |
| STEM | 83 | 2,015,115 | 3,664,927 | 0.07 TB |
| **total** | **420** | **19,707,852** | **44,295,078** | **5.81 TB** |

A subset name that appears in more than one category is qualified with its category, for example
`Chart-CoSyn` and `Document-CoSyn`.

## Provenance of the answers

| stage | models |
|---|---|
| answer rewriting | Qwen3-VL-235B-A22B-Instruct, Kimi-K2.5, MAI-UI-8B |
| reasoning rewriting | Qwen3-VL-235B-A22B-Thinking, Kimi-K2.5, Doubao-Thinking |
| image-question consistency | Qwen3-VL-30B-A3B-Instruct |
| answer verification | Qwen3-235B-A22B-Instruct |

`sources` records which annotation pass a dialogue belongs to: `Honey-Data-15M-Rollout1` is the
re-curated original pool, `Rollout2` and `Rollout3` are the two further independent passes over the
same instructions. Dialogues taken from newly incorporated community corpora carry that corpus's
name instead.

## Licensing information

Honey-Data-V2 aggregates many publicly available datasets, each governed by its own licence. Anyone
using this corpus must observe the licence attached to each constituent dataset. To the extent we
hold any rights in the curation and the generated answers, those are released under CC-BY-4.0.

## Citation

If you use Honey-Data-V2, please cite the journal version:

```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},
}
```

The conference version, which introduced Honey-Data-15M and Bee-8B:

```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},
}
```