| --- |
| license: apache-2.0 |
| pipeline_tag: image-text-to-text |
| library_name: transformers |
| tags: |
| - internvl |
| - multimodal |
| - vision-language |
| - datacomp |
| - pretrained |
| base_model: |
| - Qwen/Qwen2.5-1.5B |
| - OpenGVLab/InternViT-300M-448px |
| datasets: |
| - mlfoundations/dcvlm-baseline-200b |
| --- |
| |
| # DCVLM-2B |
|
|
| **DCVLM-2B** is the `medium`-scale reference 2B-parameter VLM from our [DataComp-VLM](https://arxiv.org/abs/2606.28551) |
| paper, pretrained from scratch on **DCVLM-Baseline**. |
|
|
| > 鈿狅笍 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 | `medium` | |
| | Parameters ($N$) | 2B | |
| | Training tokens ($D$) | 25B | |
| | Architecture | InternVL-2.5 (`InternVLChatModel`) | |
| | Vision encoder | InternViT-300M-448px | |
| | Language model | Qwen/Qwen2.5-1.5B | |
| | Precision | bfloat16 | |
| | Training compute | ~640 H100 hours | |
| | Training data | [DCVLM-Baseline](https://huggingface.co/datasets/mlfoundations/dcvlm-baseline-200b) | |
| | 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 | 55.3 | 62.6 | 51.9 | 45.8 | 40.6 | 46.3 | 50.6 | |
| | **DCVLM-Baseline (this model)** | 62.3 | 60.5 | 45.8 | 47.3 | 44.2 | 47.8 | **51.7** | |
|
|
| Categories: **Gen** general understanding 路 **Know** knowledge 路 **OCR** OCR & charts 路 |
| **Vision** vision-centric 路 **MTL** multilingual 路 **Text** text-only. |
|
|
| ## Usage |
|
|
| The model uses custom modeling code, so `trust_remote_code=True` is required. |
|
|
| ```python |
| import torch |
| from transformers import AutoModel, AutoTokenizer |
| |
| path = "mlfoundations/dcvlm-2b-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](https://github.com/mlfoundations/dcvlm) | |
| | Datasets | [DCVLM-Baseline 6.25B](https://huggingface.co/datasets/mlfoundations/dcvlm-baseline-6_25b) 路 [DCVLM-Baseline 200B](https://huggingface.co/datasets/mlfoundations/dcvlm-baseline-200b) 路 [DCVLM-Balanced 200B](https://huggingface.co/datasets/mlfoundations/dcvlm-balanced-200b) | |
| | Models | [1B](https://huggingface.co/mlfoundations/dcvlm-1b-model) 路 [2B](https://huggingface.co/mlfoundations/dcvlm-2b-model) 路 [4B](https://huggingface.co/mlfoundations/dcvlm-4b-model) 路 [8B](https://huggingface.co/mlfoundations/dcvlm-8b-model) | |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
