Aitana-2B-S-tourism-Instruct is an instruction-tuned generative language model from the Aitana family, developed by the GPLSI (Language and Information Systems Group) at the University of Alicante. Built on gplsi/Aitana-2B-S-tourism-base, this model has been fine-tuned to follow instructions across Valencian, Spanish, and English, with specialized capabilities for tourism domain applications.
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:
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
importtorchfromtransformersimportpipeline,AutoTokenizermodel_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 exampletext="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 exampletext="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 exampletext="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'])
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.
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:
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.
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
@misc{gplsi-Aitana-2B-S-tourism-Instruct,author={Martínez-Murillo, Iván and Sepúlveda-Torres, Robiert and Grande, Eduardo and Galiano, Santiago and Estevanell-Valladares, Ernesto L. 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}}
