VillanovaAI/Villanova-2B-VL-2603
Model Card for Villanova-2B-VL-2603
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Villanova-2B-VL-2603 is a fully open, multilingual Vision-Language Model developed by Villanova.AI. Part of the Villanova project, it extends our text-only Villanova-2B-2603 to visual understanding while preserving native support for five European languages. All model weights, training data sources, and training details are publicly released.
Built on a LLaVA-style architecture pairing a SigLIP vision encoder with the Villanova-2B-Base-2603 language backbone, this ~2.8B-parameter model delivers strong multimodal understanding, visual question answering, and multilingual image captioning under a fully open Apache 2.0 license.
Model Family
[Villanova-2B-Base-2603](https://huggingface.co/VillanovaAI/Villanova-2B-Base-2603) — Base model (4.4T)<br>  ↳ [Villanova-2B-2603](https://huggingface.co/VillanovaAI/Villanova-2B-2603) — SFT / Instruct<br>   ↳ Villanova-2B-2603-GGUF — Quantized<br>  ↳ [Villanova-2B-VL-2603](https://huggingface.co/VillanovaAI/Villanova-2B-VL-2603) — Vision-Language Instruct — 📍 This model<br>   ↳ Villanova-2B-VL-2603-GGUF — Quantized<br> <br> [Villanova-2B-Base-2512-Preview](https://huggingface.co/VillanovaAI/Villanova-2B-Base-2512-Preview) — Base model (2.2T) (previous version, not recommended)<br>  ↳ [Villanova-2B-2512-Preview](https://huggingface.co/VillanovaAI/Villanova-2B-2512-Preview) — SFT / Instruct (previous version, not recommended)<br>
Highlights
- European-focused, fully open VLM released under Apache 2.0
- Native multilingual support for 5 European languages: English, French, German, Italian, and Spanish, including multilingual image captioning (XM3600) and visual instruction following
- Broad visual understanding across general VQA (RealWorldQA, CVQA, MME) and multilingual benchmarks (Multi-MMBench, Multi-AI2D)
- Preserves text-only capabilities of the Villanova-2B-2603 language backbone through text-only data mixing in Stage 2
- Only ~2.8B parameters, efficient enough for single-GPU inference
Model Summary
Training Recipe (Stage 1: Projector Alignment)
Stage 1 aligns the vision encoder output to the language model embedding space by training only the multimodal projector, with both the vision tower and the LLM fully frozen. This is a lightweight warmup that teaches the projector how to map SigLIP visual features into the Villanova-2B token space before any instruction tuning.
Data: Multi-Pixmo-Cap, multilingual image caption pairs in EN/DE/ES/FR/IT (brief captions split).
Training Data
Both stages use only permissively-licensed data (no GPT/Claude-generated content). The curated multilingual derivatives (the Multi-* datasets, translated and post-processed in EN/DE/ES/FR/IT) are released by Villanova.AI on the HuggingFace Hub.
Stage 1: Projector Alignment (~600K samples)
Stage 2: Visual Instruction Tuning (~1.08M samples)
The text-only mixing in Stage 2 prevents catastrophic forgetting of the language model's pre-existing capabilities.
Training Recipe (Stage 2: Visual Instruction Tuning)
How to Use
import torch
from PIL import Image
from transformers import AutoProcessor, LlavaForConditionalGeneration
model_name = "VillanovaAI/Villanova-2B-VL-2603"
device = "cuda"
processor = AutoProcessor.from_pretrained(model_name)
model = LlavaForConditionalGeneration.from_pretrained(
model_name,
dtype=torch.bfloat16,
).to(device)
model.eval()
image = Image.open("example.jpg").convert("RGB")
# The `<image>` placeholder inside the content string marks where the
# image tokens will be inserted by the processor.
messages = [
{"role": "user", "content": "<image>\nDescribe this image in detail."},
]
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(images=image, text=prompt, return_tensors="pt").to(device, torch.bfloat16)
with torch.no_grad():
output_ids = model.generate(**inputs, max_new_tokens=256, do_sample=False)
response = processor.decode(output_ids[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)Evaluation
Villanova-2B-VL-2603 was evaluated using VLMEvalKit on a suite of standard and multilingual VLM benchmarks covering multiple-choice reasoning, general visual question answering, hallucination robustness, and cross-lingual visual understanding. All evaluations use exact_matching judging (no LLM-as-judge) for full reproducibility.
We compare against [Salamandra-VL-7B](https://huggingface.co/BSC-LT/Salamandra-VL-7B-2512), a strong European VLM built on the same language family.
Despite using less than a third of the parameters (~2.8B vs ~8.9B), Villanova-2B-VL-2603 matches Salamandra-VL-7B overall, with particular strengths on general VQA and multilingual benchmarks. Multilingual benchmarks are reported as the average across EN/DE/ES/FR/IT.
The Overall row is the unweighted average across the 10 benchmarks on the 0-100 scale. MME and OCRBench are excluded because they use different scoring scales (0-2800 and 0-1000 respectively).
Multilingual Evaluation (Per-Language Detail)
The multilingual benchmarks (Multi-MMBench, Multi-AI2D, Multi-MMStar) are extensions of the standard benchmarks with parallel test sets in 5 European languages. Below is the per-language breakdown.
Key takeaways:
- Competitive overall average (53.9 vs 54.8) against a model with ~3.2x more parameters
- Wins on general VQA: RealWorldQA, CVQA, and MME all outperform Salamandra-VL-7B
- Solid multilingual capability across EN/DE/ES/FR/IT, with a particularly strong Multi-AI2D improvement (+8.0 avg, wins on all 5 languages) over Salamandra-VL-7B
- Balanced per-language performance: on Multi-AI2D and Multi-MMBench, Villanova performs uniformly across DE/EN/ES/FR/IT (no language collapse)
Intended Use
- Multilingual image captioning and description
- Visual question answering (single-image)
- Document and chart understanding (OCR-light tasks)
- Multimodal instruction following in EN/DE/ES/FR/IT
- Research on fully-open European VLMs
Limitations
- Single-image inference only (no multi-image or video support)
- OCR quality on dense, small-text documents is limited compared to specialized OCR-heavy VLMs
- As with all VLMs, outputs can contain hallucinations; users should verify factual claims
License
This model is released under the Apache 2.0 License. The training data used for Stage 2 was selected to allow permissive commercial use (no GPT/Claude-generated content).
Citation
@inproceedings{simonazzi2026villanova2b,
author = {Simonazzi, Stefano and Zeinalipour, Kamyar and Gabburo, Matteo and Baiamonte, Daniela and Fano, Elena and Zugarini, Andrea},
title = {Villanova-2B: European Open Multilingual and Multimodal Language Models},
booktitle = {Proceedings of the Twelfth Italian Conference on Computational Linguistics (CLiC-it 2026)},
editor = {Basile, Valerio and Croce, Danilo and Passaro, Lucia and Pirrone, Roberto},
year = {2026},
note = {In press}
}
@article{baiamonte2026multilingual,
title={Multilingual Training and Evaluation Resources for Vision-Language Models},
author={Baiamonte, Daniela and Fano, Elena and Gabburo, Matteo and Simonazzi, Stefano and Rigutini, Leonardo and Zugarini, Andrea},
journal={arXiv preprint arXiv:2604.18347},
year={2026}
}