Model Card for Villanova-2B-VL-2603

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 — Base model (4.4T)  ↳ Villanova-2B-2603 — SFT / Instruct   ↳ Villanova-2B-2603-GGUF — Quantized  ↳ Villanova-2B-VL-2603 — Vision-Language Instruct — 📍 This model   ↳ Villanova-2B-VL-2603-GGUF — Quantized Villanova-2B-Base-2512-Preview — Base model (2.2T) (previous version, not recommended)  ↳ Villanova-2B-2512-Preview — SFT / Instruct (previous version, not recommended)

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, 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}
}