Instructions to use mlfoundations/dcvlm-1b-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlfoundations/dcvlm-1b-model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="mlfoundations/dcvlm-1b-model", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mlfoundations/dcvlm-1b-model", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mlfoundations/dcvlm-1b-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlfoundations/dcvlm-1b-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlfoundations/dcvlm-1b-model",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/mlfoundations/dcvlm-1b-model

SGLang

How to use mlfoundations/dcvlm-1b-model with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mlfoundations/dcvlm-1b-model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlfoundations/dcvlm-1b-model",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mlfoundations/dcvlm-1b-model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlfoundations/dcvlm-1b-model",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use mlfoundations/dcvlm-1b-model with Docker Model Runner:
```
docker model run hf.co/mlfoundations/dcvlm-1b-model
```

DCVLM-1B

DCVLM-1B is the small-scale reference 1B-parameter VLM from our DataComp-VLM paper, pretrained from scratch on DCVLM-Baseline.

⚠️ This is a pretrained (base) model, not an instruction-tuned assistant. It is released as a reproducible reference point for data-centric research: to compare pretraining datasets, and as an initialization for your own SFT. It has not been chat- or preference-tuned and should not be expected to follow instructions reliably.

Model details


Scale	`small`
Parameters ($N$)	1B
Training tokens ($D$)	6.25B
Architecture	InternVL-2.5 (`InternVLChatModel`)
Vision encoder	InternViT-300M-448px
Language model	Qwen/Qwen2.5-0.5B
Precision	bfloat16
Training compute	~80 H100 hours
Training data	DCVLM-Baseline
Data mixture	10% captioning / 70% visual instruction / 15% text-only / 5% multimodal documents

Results

Core evaluation suite (33 tasks), compared against FineVision, the previous best open pretraining dataset, trained at the same scale with the same architecture and token budget.

Method	Gen	Know	OCR	Vision	MTL	Text	Core Avg
FineVision	40.1	45.6	35.0	41.0	28.2	28.9	36.2
DCVLM-Baseline (this model)	40.5	43.6	33.0	39.1	25.4	34.7	36.5

Categories: Gen general understanding · Know knowledge · OCR OCR & charts · Vision vision-centric · MTL multilingual · Text text-only.

Usage

The model uses custom modeling code, so trust_remote_code=True is required.

import torch
from transformers import AutoModel, AutoTokenizer

path = "mlfoundations/dcvlm-1b-model"
model = AutoModel.from_pretrained(
    path, torch_dtype=torch.bfloat16, trust_remote_code=True, low_cpu_mem_usage=True
).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)

Image preprocessing follows the standard InternVL-2.5 dynamic-tiling recipe (448px tiles, max_dynamic_patch=12).

Related releases


Code	github.com/mlfoundations/dcvlm
Datasets	DCVLM-Baseline 6.25B · DCVLM-Baseline 200B · DCVLM-Balanced 200B
Models	1B · 2B · 4B · 8B

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}
}