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VillanovaAI
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Model Details
Model Provider
VillanovaAI
Model Tree
VillanovaAI/Villanova-2B-Base-2603
Input Modalities
Output Modalities
Supported Functionality
Villanova-2B-Base-2603 — Base model (4.4T) ↳ Villanova-2B-2603 — SFT / Instruct — 📍 This model ↳ Villanova-2B-2603-GGUF — Quantized ↳ Villanova-2B-VL-2603 — Vision-Language Instruct ↳ 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)
| Architecture | Decoder-only Transformer (LLaMA-based) |
| Parameters | 2.4B |
| Base Model | VillanovaAI/Villanova-2B-Base-2603 (pretrained from scratch) |
| Pre-training Data | 4.4T tokens (multilingual, two-stage) |
| Fine-tuning Data | VillanovaAI/villanova-sft-2603 |
| Languages | English, French, German, Italian, Spanish |
| Context Length | 32,768 tokens |
| Precision | bfloat16 |
| License | Apache 2.0 |
python
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "VillanovaAI/Villanova-2B-2603"device = "cuda" # or "cpu" tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name).to(device) messages = [ {"role": "user", "content": "Explain quantum entanglement in simple terms."}] input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)model_inputs = tokenizer([input_text], return_tensors="pt").to(device) generated_ids = model.generate(**model_inputs, max_new_tokens=256, do_sample=True, temperature=0.7)output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]print(tokenizer.decode(output_ids, skip_special_tokens=True))Villanova-2B-2603 was extensively evaluated across 25 benchmarks covering Reasoning, Question Answering, Safety, and Instruction Following in both English and multilingual settings. All evaluations were performed using identical settings and prompts for fair comparison.
Tables are sorted by the main metric (descending). Models are grouped into Fully Open and Open Weight categories.
Villanova-2B-2603 is the #1 fully open model in overall average across all benchmarks.
| Model | Size | Reasoning | QA | Safety | Instr. Follow | Overall |
|---|---|---|---|---|---|---|
| Fully Open | ||||||
| Villanova-2B-2603 | 2.4B | 31.0 | 33.1 | 39.5 | 45.1 | 36.9 |
| OLMo-2-0425-1B-Instruct | 1.2B | 38.7 | 35.6 | 19.4 | 39.3 | 33.9 |
| Minerva-7B-instruct-v1.0 | 7.4B | 27.1 | 36.2 | 30.1 | 16.9 | 28.5 |
| EuroLLM-1.7B-Instruct | 1.7B | 26.0 | 24.7 | 3.8 | 19.5 | 19.5 |
| salamandra-2b-instruct | 2.3B | 23.6 | 26.6 | 9.6 | 15.7 | 20.0 |
| Open Weight | ||||||
| Llama-3.2-3B-Instruct | 3.2B | 51.2 | 48.1 | 56.8 | 48.1 | 50.4 |
| Qwen2.5-3B-Instruct | 3.1B | 39.4 | 35.8 | 54.7 | 46.8 | 42.9 |
| Llama-3.2-1B-Instruct | 1.2B | 37.5 | 38.1 | 56.6 | 35.5 | 41.1 |
| gemma-3-1b-it | 1.0B | 28.5 | 27.0 | 53.6 | 39.9 | 35.7 |
| Qwen3-1.7B | 1.7B | 37.4 | 37.5 | 2.6 | 19.5 | 26.2 |
Villanova-2B-2603 is the #1 fully open model for instruction following, and is competitive with larger open weight models. The MARCO benchmark evaluates structured instruction following across all five languages.
| Model | Size | IFEval | MARCO-EN | MARCO-DE | MARCO-ES | MARCO-FR | MARCO-IT | Avg |
|---|---|---|---|---|---|---|---|---|
| Fully Open | ||||||||
| Villanova-2B-2603 | 2.4B | 62.0 | 39.4 | 40.5 | 44.2 | 42.5 | 42.1 | 45.1 |
| OLMo-2-0425-1B-Instruct | 1.2B | 77.9 | 52.9 | 23.1 | 29.0 | 27.9 | 24.9 | 39.3 |
| EuroLLM-1.7B-Instruct | 1.7B | 34.5 | 18.3 | 15.9 | 15.9 | 17.4 | 15.2 | 19.5 |
| Minerva-7B-instruct-v1.0 | 7.4B | 29.6 | 17.0 | 12.2 | 13.9 | 13.9 | 15.0 | 16.9 |
| salamandra-2b-instruct | 2.3B | 26.4 | 17.7 | 12.2 | 12.0 | 12.9 | 12.9 | 15.7 |
| Open Weight | ||||||||
| Llama-3.2-3B-Instruct | 3.2B | 82.2 | 54.0 | 39.9 | 38.8 | 37.5 | 35.9 | 48.1 |
| Qwen2.5-3B-Instruct | 3.1B | 71.5 | 47.3 | 37.5 | 42.5 | 41.0 | 40.7 | 46.8 |
| gemma-3-1b-it | 1.0B | 74.5 | 42.7 | 27.5 | 33.3 | 27.9 | 33.3 | 39.9 |
| Llama-3.2-1B-Instruct | 1.2B | 64.8 | 43.2 | 25.3 | 29.0 | 24.2 | 26.6 | 35.5 |
| Qwen3-1.7B | 1.7B | 48.4 | 27.4 | 8.9 | 10.3 | 13.1 | 9.1 | 19.5 |
Key insight: While some models score higher on English-only IFEval, Villanova-2B-2603 delivers the most balanced multilingual instruction following, with MARCO scores of 40-44 across DE, ES, FR, IT. This is far ahead of OLMo (19-25) and Gemma (27-33) on non-English languages.
Villanova-2B-2603 is the #1 fully open model for safety. Safety was evaluated using the M-ALERT benchmark across all five languages.
| Model | Size | EN | DE | ES | FR | IT | Avg |
|---|---|---|---|---|---|---|---|
| Fully Open | |||||||
| Villanova-2B-2603 | 2.4B | 31.0 | 4.1 | 56.0 | 62.2 | 44.2 | 39.5 |
| Minerva-7B-instruct-v1.0 | 7.4B | 31.6 | 4.3 | 26.9 | 24.8 | 62.9 | 30.1 |
| OLMo-2-0425-1B-Instruct | 1.2B | 58.0 | 5.7 | 13.4 | 10.7 | 9.1 | 19.4 |
| salamandra-2b-instruct | 2.3B | 4.9 | 3.0 | 15.6 | 15.4 | 9.2 | 9.6 |
| EuroLLM-1.7B-Instruct | 1.7B | 5.4 | 0.8 | 2.6 | 8.4 | 1.7 | 3.8 |
| Open Weight | |||||||
| Llama-3.2-3B-Instruct | 3.2B | 54.5 | 26.4 | 70.3 | 63.3 | 69.4 | 56.8 |
| Llama-3.2-1B-Instruct | 1.2B | 47.1 | 32.9 | 67.4 | 68.6 | 67.2 | 56.6 |
| Qwen2.5-3B-Instruct | 3.1B | 60.2 | 23.2 | 71.7 | 64.0 | 54.4 | 54.7 |
| gemma-3-1b-it | 1.0B | 58.6 | 28.7 | 58.8 | 68.4 | 53.3 | 53.6 |
| Qwen3-1.7B | 1.7B | 10.2 | 0.0 | 0.5 | 0.8 | 1.3 | 2.6 |
| Model | Size | BBH | LB-BBH | GSM8K | DROP | TruthfulQA | Avg Reasoning | Avg QA |
|---|---|---|---|---|---|---|---|---|
| Fully Open | ||||||||
| Minerva-7B-instruct-v1.0 | 7.4B | 29.0 | 30.0 | 10.6 | 29.2 | 29.6 | 27.1 | 36.2 |
| OLMo-2-0425-1B-Instruct | 1.2B | 27.6 | 33.8 | 67.4 | 30.2 | 33.8 | 38.7 | 35.6 |
| Villanova-2B-2603 | 2.4B | 29.3 | 33.2 | 23.4 | 34.8 | 28.5 | 31.0 | 33.1 |
| salamandra-2b-instruct | 2.3B | 22.5 | 29.2 | 2.3 | 20.6 | 27.8 | 23.6 | 26.6 |
| EuroLLM-1.7B-Instruct | 1.7B | 28.5 | 29.8 | 12.7 | 22.2 | 29.2 | 26.0 | 24.7 |
| Open Weight | ||||||||
| Llama-3.2-3B-Instruct | 3.2B | 59.3 | 44.6 | 77.2 | 48.3 | 36.1 | 51.2 | 48.1 |
| Qwen2.5-3B-Instruct | 3.1B | 12.2 | 46.9 | 76.0 | 12.5 | 41.4 | 39.4 | 35.8 |
| Qwen3-1.7B | 1.7B | 9.8 | 43.5 | 74.2 | 34.4 | 29.6 | 37.4 | 37.5 |
| Llama-3.2-1B-Instruct | 1.2B | 39.3 | 35.7 | 45.6 | 31.8 | 28.9 | 37.5 | 38.1 |
| gemma-3-1b-it | 1.0B | 25.0 | 35.1 | 34.0 | 21.1 | 26.6 | 28.5 | 27.0 |
Villanova-2B-2603 represents a major leap over our previous model (Villanova-2B-2512-Preview):
| Category | 2512-Preview | 2603 | Improvement |
|---|---|---|---|
| Overall | 23.3 | 36.9 | +58% |
| Instruction Following | 28.9 | 45.1 | +56% |
| Safety | 2.4 | 39.5 | +1546% |
| Reasoning | 27.5 | 31.0 | +13% |
| QA | 29.0 | 33.1 | +14% |
This model is released under the Apache 2.0 License.
text
@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}}from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "VillanovaAI/Villanova-2B-2603"device = "cuda" # or "cpu" tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name).to(device) messages = [ {"role": "user", "content": "Explain quantum entanglement in simple terms."}] input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)model_inputs = tokenizer([input_text], return_tensors="pt").to(device) generated_ids = model.generate(**model_inputs, max_new_tokens=256, do_sample=True, temperature=0.7)output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]print(tokenizer.decode(output_ids, skip_special_tokens=True))@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}}