Table of Contents

• Model Description
• Evaluation
• Training Data
• Intended Uses
• How to Use
• GGUF for LM Studio
• Additional Information

Model Description

Aitana-2B-S-base extends the multilingual Salamandra foundation with additional training on domain-specific Valencian, Spanish, and English data. The training emphasizes administrative, legal, and tourism domains.

Training Data

This model was trained on the following ALIA datasets:

Data Sources

• DOGV (Diari Oficial de la Generalitat Valenciana): Official communications of the Valencian Community including laws and public sector communications
• Les Corts Valencianes: Transcripts from the Valencian Parliament plenary sessions and committee meetings
• AMIC: Valencian language corpus
• BOUA (Butlletí Oficial de la Universitat d'Alacant): Official University of Alicante documents including grants, regulations, and resolutions
• Tourism: Multilingual tourism domain content

Intended Uses

This model can be used for:

• Text generation in Valencian, Spanish, and English
• Fine-tuning for specific downstream tasks
• Domain adaptation for administrative, legal, or tourism applications

Note: Due to the formal register of training data (administrative and legal domains), generated text tends toward formal language.

How to Use

Transformers

python
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import torch
from transformers import pipeline, AutoTokenizer

model_id = "gplsi/Aitana-2B-S-base"
tokenizer = AutoTokenizer.from_pretrained(model_id)

generator = pipeline(
    "text-generation",
    model=model_id,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

# Valencian example
text = "Les corts valencianes han pres la decisió de"
result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
print(result[0]['generated_text'])

# Spanish example  
text = "El turismo en la Comunidad Valenciana"
result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
print(result[0]['generated_text'])

GGUF for LM Studio

This repository includes GGUF quantized versions for use with LM Studio, Ollama, and other llama.cpp-based tools.

Using with llama-cpp-python

python
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from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="gplsi/Aitana-2B-S-base",
    filename="Aitana-s2b-c0dc17-Q4_K_M.gguf",
)

output = llm("Les corts valencianes han decidit", max_tokens=100)
print(output["choices"][0]["text"])

Evaluation

In the following table, we can see the results obtained with different benchmarks from lm-evaluation-harness in comparison with the model used for continuous pre-training. The results have been obtained from the model pre-trained; no instruction tuning or fine-tuning of any kind has been performed.

Normalized score per language

Valencian

Classification Benchmarks

Generation Benchmarks

Catalan

Classification Benchmarks

Generation Benchmarks

Spanish

Classification Benchmarks

Generation Benchmarks

English

Classification Benchmarks

Additional Information

Author

The model has been developed by the Language and Information Systems Group (GPLSI) and the Centro de Inteligencia Digital (CENID), both part of the University of Alicante (UA), as part of their ongoing research in Natural Language Processing (NLP).

Part of the Aitana Family

This model is part of the Aitana model family, which includes:

• gplsi/Aitana-2B-S - Valencian-focused base model
• gplsi/Aitana-TA-2B-S - Translation model (Spanish ↔ Valencian)

Funding

This work is funded by the Ministerio para la Transformación Digital y de la Función Pública, co-financed by the EU – NextGenerationEU, within the framework of the project Desarrollo de Modelos ALIA. This work has also been partially supported by Project HEART-NLP (PID2024-156263OB-C22).

Acknowledgments

We would like to express our gratitude to all individuals and institutions that have contributed to the development of this work.

Special thanks to:

• Language Technologies Laboratory at Barcelona Supercomputing Center
• Centro Vasco de Tecnología de la Lengua (HiTZ)
• Centro Singular de Investigación en Tecnologías Inteligentes (CiTIUS)
• Sistemas Inteligentes de Acceso a la Información (SINAI)
• Instituto Universitario de Investigación Informática (IUII)
• Leonardo HPC System
• European supercomputing ecosystem (EUROHPC)

We also acknowledge the financial, technical, and scientific support of the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project Desarrollo de Modelos ALIA, whose contribution has been essential to the completion of this research.

License

Apache License, Version 2.0

Disclaimer

This model is intended for general purposes and is available under a permissive Apache License 2.0. Be aware that the model may have biases and/or undesirable outputs. Users deploying systems based on this model are responsible for mitigating risks and complying with applicable AI regulations.

Reference

bibtex
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@misc{gplsi-Aitana-2B-S-base,
  author       = {Estevanell-Valladares, Ernesto L. and Yáñez-Romero, Fabio and Sepúlveda-Torres, Robiert and Consuegra-Ayala, Juan Pablo and Galeano, Santiago and Miró Maestre, María and Martínez-Murillo, Iván and Grande, Eduardo and Canal-Esteve, Miquel and Bonora, Mar and Gutierrez, Yoan and Abreu Salas, José Ignacio and Lloret, Elena and Montoyo, Andrés and Muñoz-Guillena and Palomar, Manuel},
  title        = {Aitana 2B base: Continually pre-trained on Valencian},
  year         = {2025},
  institution  = {Language and Information Systems Group (GPLSI) and Centro de Inteligencia Digital (CENID), University of Alicante (UA)},
  howpublished = {\url{https://huggingface.co/gplsi/gplsi/Aitana-2B-S-base}},
  note         = {Accessed: 2025-12-12}
}

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Copyright © 2025 Language and Information Systems Group (GPLSI) and Centro de Inteligencia Digital (CENID), University of Alicante (UA). Distributed under the Apache License 2.0.