mlfoundations/dcvlm-balanced-200b

DCVLM-Balanced (200B tokens)

🚨🚨🚨 NOTE: Upload in progress — the shard set is still being populated and is not yet complete. Please check back for the full release.

DCVLM-Balanced is the balanced-mixture training set from our DataComp-VLM paper. It is a pre-mixed, decontaminated, ready-to-train multimodal pretraining dataset, materialized as flat WebDataset tar shards so it can be consumed by any training stack.

This is a 200B-token release consisting of 112,358,849 samples, curated from our DCVLM-large data pool. The instruction-heavy counterpart (DCVLM-baseline) is available as dcvlm-baseline-200b, along with a smaller 6.25B-token version.

⚠️ NOTE: The training data is the WebDataset shards under shards/. The preview config shown in the Dataset Viewer above is a small, downsampled rendering built purely so the HF-Hub can display examples---it is not the dataset you train on. See Loading the dataset.

The mixture

The mixture matches our paper's Balanced mix. Proportions are by training samples:

Data type	Ratio	Description
captioning	40%	Single image-captioning data
instruction	40%	Visual instruction / VQA / multi-turn chat
text	15%	Text-only instruction data
mmdoc_mt	5%	Interleaved multimodal documents (multi-image)

Repository layout

shards/000/000000.tar … 000999.tar   # THE dataset: 11,318 WebDataset shards, spread over
shards/001/001000.tar … 001999.tar   # 12 subdirectories of 1,000. Shard numbering is 
…                                    # gobal: 
shards/011/011000.tar … 011317.tar   # shard i lives in shards/{i // 1000}/.
preview/preview-0000.parquet         # small rendered sample, for the Dataset Viewer only
dataset_info.json                    # provenance: counts, token totals, seed
README.md

Sample schema (WebDataset shards)

Each training example is one WebDataset sample: a group of members sharing a common key. Members:

Member	Type	Meaning
conversations.txt	text	The conversation (see format below). Present in every sample.
0.jpg, 1.jpg, …	image	The images, 0-indexed in the order they are referenced. The N-th <image> token → N.jpg.
data_type.txt	text	instruction / text / captioning / mmdoc_mt.
ds_name.txt	text	Originating source dataset (provenance).
orig_url.txt	text	Original source shard URL (provenance).
ntok.txt	text	Training token count for this sample (full multimodal count, including image tiles).
mdp.txt	text	max_dynamic_patch — the image-tiling parameter used for this sample's source (provenance).

Text-only samples (data_type == text) carry no image members. Multi-image samples (mmdoc_mt, and some instruction data) carry several N.jpg/png/webp members.

conversations.txt format

Turns are stored as role:value, joined by the separator "<EOCL>\n" (a literal <EOCL> followed by a newline). Roles are human and gpt. <image> placeholder tokens appear inline inside the turn text. The N-th <image> corresponds to image member N.jpg.

Example (a single image-captioning sample):

human:<image>
Describe the image concisely.<EOCL>
gpt:Fiddlenecks, aka Devil's lettuce

This is the ShareGPT-style turn format serialized to a single string. The helper below converts it back into a standard list of turn dicts ([{"from": ..., "value": ...}, …]).

Loading the dataset

Parse conversations.txt → JSON turns

EOL_SEPARATOR = "<EOCL>\n"

def txt2conversations(txt: str) -> list[dict]:
    """Returns ShareGPT-style turns: [{"from": "human"|"gpt", "value": str}, ...]."""
    conversations = []
    for line in txt.split(EOL_SEPARATOR):
        if line == "":
            continue
        role = line.split(":", 1)[0]          # role is prepended before the first ':'
        value = line[len(role) + 1:]          # everything after that ':' is the value
        conversations.append({"from": role, "value": value})
    return conversations

Stream the training data with the webdataset library (recommended)

import webdataset as wds
from huggingface_hub import get_token

REPO = "mlfoundations/dcvlm-balanced-200b"
BASE = f"https://huggingface.co/datasets/{REPO}/resolve/main"
NUM_SHARDS, PER_DIR = 11318, 1000

# Shards are grouped 1,000 per subdirectory, so build the list explicitly rather than
# with a single brace pattern: shard i is at shards/{i // PER_DIR:03d}/{i:06d}.tar
urls = [f"{BASE}/shards/{i // PER_DIR:03d}/{i:06d}.tar" for i in range(NUM_SHARDS)]
urls = [f"pipe:curl -s -L {u} -H 'Authorization:Bearer {get_token()}'" for u in urls]

dataset = wds.WebDataset(urls, shardshuffle=True).shuffle(1000).decode("pilrgb")

for sample in dataset:
    conversations = txt2conversations(sample["conversations.txt"].decode())
    images = [sample[k] for k in sorted(sample) if k.endswith(".jpg")]  # PIL, in <image> order
    data_type = sample["data_type.txt"].decode()
    # ... tokenize / collate for your trainer ...

Browse the rendered preview

The preview config is a small, downsampled slice for quick inspection. It is not the training data.

from datasets import load_dataset
prev = load_dataset("mlfoundations/dcvlm-balanced-200b", "preview", split="train")
prev[0]  # {"images": [PIL...], "conversations": [{"from","value"},...], "data_type", "ds_name", ...}

Citation

@article{farina2026datacomp,
  title={DataComp-VLM: Improved Open Datasets for Vision-Language Models},
  author={Farina, Matteo and Udandarao, Vishaal and Nguyen, Thao and Kuzucu, Selim and B{\"o}ther, Maximilian and Hochlehnert, Andreas and Ghosh, Adhiraj and Nezhurina, Marianna and Roth, Karsten and Struber, Joschka and others},
  journal={arXiv preprint arXiv:2606.28551},
  year={2026}
}