Table of Contents

• Model Description
• Training Data
• Intended Uses
• How to Use
• Evaluation
• Additional Information

Model Description

Aitana-2B-S-tourism-Instruct extends the Aitana-2B-S-tourism-base domain-specific foundation model with instruction fine-tuning. This combination makes it particularly well-suited for tourism-related tasks requiring instruction following in Valencian, Spanish, and English.

Training Data

This model was instruction fine-tuned using the following data:

Intended Uses

This model can be used for:

• Tourism text generation in Valencian, Spanish, and English
• Travel content creation and visitor assistance
• Instruction following with tourism domain expertise
• Fine-tuning for specific tourism downstream tasks

Note: This model combines tourism domain specialization with instruction-following capabilities. For general-purpose instruction following, consider other models in the Aitana family.

How to Use

Transformers

python
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import torch
from transformers import pipeline, AutoTokenizer

model_id = "gplsi/Aitana-2B-S-tourism-Instruct"
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 = "Recomana'm les millors platges de la Costa Blanca per a unes vacances familiars."
result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
print(result[0]['generated_text'])
# Spanish example
text = "Describe los principales atractivos turísticos de la Comunidad Valenciana."
result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
print(result[0]['generated_text'])
# English example
text = "What are the best cultural sites to visit in Valencia?"
result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
print(result[0]['generated_text'])

Evaluation

In the following tables, we present the results obtained with different benchmarks from lm-evaluation-harness in comparison with Salamandra-2B-Instruct.

Normalized score per language

Valencian

Classification Benchmarks

Generation Benchmarks

Catalan

Classification Benchmarks

Generation Benchmarks

Spanish

Classification Benchmarks

Generation Benchmarks

English

Classification Benchmarks

Judge Evaluation

The model was also evaluated using an LLM-as-judge approach across different task categories. The scores below represent the average rating (1-5 scale, 5 being best) and standard deviation for each task category, comparing against Salamandra-2B-Instruct.

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).

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-tourism-Instruct,
  author       = {Martínez-Murillo, Iván and Sepúlveda-Torres, Robiert and Grande, Eduardo and Galiano, Santiago and Consuegra-Ayala, Juan Pablo and Miró Maestre, María and Canal-Esteve, Miquel and Bonora, Mar and Gutierrez, Yoan and Abreu Salas, José Ignacio and Lloret, Elena and Montoyo, Andrés and Muñoz-Guillena, Rafael and Palomar, Manuel},
  title        = {Aitana 2B Tourism Instruct: Instruction-tuned model for tourism applications in Valencian, Spanish and English},
  year         = {2026},
  institution  = {Language and Information Systems Group (GPLSI) and Centro de Inteligencia Digital (CENID), University of Alicante (UA)},
  howpublished = {\url{https://huggingface.co/gplsi/Aitana-2B-S-tourism-Instruct}},
  note         = {Accessed: 2026-05-21}
}

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Copyright © 2026 Language and Information Systems Group (GPLSI) and Centro de Inteligencia Digital (CENID), University of Alicante (UA). Distributed under the Apache License 2.0.