Table of Contents
Model Description
Table with columns: Property, Value| Property | Value |
|---|
| Base Model | gplsi/Aitana-2B-S-IP-Instruct |
| Architecture | Transformer decoder-only |
| Parameters | ~2.25B |
| Languages | Valencian, Spanish, English |
| License | Apache 2.0 |
Aitana-2B-S-IP-Instruct is an instruction-tuned variant of Aitana-2B-S-IP-Instruct, fine-tuned on multilingual instruction data with emphasis on intellectual property applications.
Training Data
This model was instruction fine-tuned using the following data:
Intended Uses
This model can be used for:
- Instruction following in Valencian, Spanish, and English
- Intellectual property domain applications
- Chat and conversational applications requiring multilingual support
- Text generation with task-specific prompting
How to Use
import torch
from transformers import pipeline, AutoTokenizer
model_id = "gplsi/Aitana-2B-S-IP-Instruct-IP"
tokenizer = AutoTokenizer.from_pretrained(model_id)
generator = pipeline(
"text-generation",
model=model_id,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
device_map="auto",
)
text = "Explica què és la propietat intel·lectual i quins drets atorga."
result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
print(result[0]['generated_text'])
text = "Describe los principales tipos de propiedad intelectual y su marco legal."
result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
print(result[0]['generated_text'])
text = "Explain the concept of intellectual property and its importance in innovation."
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
Table with columns: Language, Salamandra-2B-Instruct, Aitana-2B-S-IP-Instruct| Language | Salamandra-2B-Instruct | Aitana-2B-S-IP-Instruct |
|---|
| Spanish | 0.079 | 0.112 |
| Catalan | 0.202 | 0.182 |
| English | 0.178 | 0.167 |
| Valencian | 0.507 | 0.489 |
| Average | 0.242 | 0.237 |
Valencian
Classification Benchmarks
Table with columns: Dataset, Lang., Task, Metric, Salamandra-2B-Instruct, Aitana-2B-S-IP-Instruct| Dataset | Lang. | Task | Metric | Salamandra-2B-Instruct | Aitana-2B-S-IP-Instruct |
|---|
| XNLI | va | Natural Language Inference | acc | 0.520 | 0.501 |
Generation Benchmarks
Table with columns: Dataset, Lang., Task, Metric, Salamandra-2B-Instruct, Aitana-2B-S-IP-Instruct| Dataset | Lang. | Task | Metric | Salamandra-2B-Instruct | Aitana-2B-S-IP-Instruct |
|---|
| Cocoteros | va | Reading Comprehension | bleu | 2.796 | 3.204 |
| Phrases ca-va | va-ca | Translation - Adaptation | bleu | 58.425 | 58.694 |
| Phrases va-ca | va-ca |
Catalan
Classification Benchmarks
Table with columns: Dataset, Lang., Task, Metric, Salamandra-2B-Instruct, Aitana-2B-S-IP-Instruct| Dataset | Lang. | Task | Metric | Salamandra-2B-Instruct | Aitana-2B-S-IP-Instruct |
|---|
| Belebele Cat_latn | ca | Reading Comprehension | acc | 0.287 | 0.253 |
| COPA | ca | Commonsense Reasoning | acc | 0.708 | 0.706 |
| XStoryCloze | ca |
Generation Benchmarks
Table with columns: Dataset, Lang., Task, Metric, Salamandra-2B-Instruct, Aitana-2B-S-IP-Instruct| Dataset | Lang. | Task | Metric | Salamandra-2B-Instruct | Aitana-2B-S-IP-Instruct |
|---|
| Cabreu abstractive | ca | Summarization | bleu | 7.610 | 8.516 |
| Cabreu extractive | ca | Summarization | bleu | 38.002 | 31.230 |
| Cabreu extreme | ca |
Spanish
Classification Benchmarks
Table with columns: Dataset, Lang., Task, Metric, Salamandra-2B-Instruct, Aitana-2B-S-IP-Instruct| Dataset | Lang. | Task | Metric | Salamandra-2B-Instruct | Aitana-2B-S-IP-Instruct |
|---|
| Belebele | es | Reading Comprehension | acc | 0.268 | 0.268 |
| PAWS | es | Paraphrasing | acc | 0.566 | 0.623 |
| XNLI | es |
Generation Benchmarks
Table with columns: Dataset, Lang., Task, Metric, Salamandra-2B-Instruct, Aitana-2B-S-IP-Instruct| Dataset | Lang. | Task | Metric | Salamandra-2B-Instruct | Aitana-2B-S-IP-Instruct |
|---|
| Cocoteros | es | Reading Comprehension | bleu | 3.308 | 2.755 |
| XLSum | es | Summarization | bleu | 1.695 | 1.474 |
English
Classification Benchmarks
Table with columns: Dataset, Lang., Task, Metric, Salamandra-2B-Instruct, Aitana-2B-S-IP-Instruct| Dataset | Lang. | Task | Metric | Salamandra-2B-Instruct | Aitana-2B-S-IP-Instruct |
|---|
| Arc Challenge | en | Question Answering | acc | 0.354 | 0.348 |
| Arc Easy | en | Question Answering | acc | 0.681 | 0.693 |
| Belebele | en |
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.
Table with columns: Task Category, Salamandra-2B-Instruct, Aitana-2B-S-IP-Instruct| Task Category | Salamandra-2B-Instruct | Aitana-2B-S-IP-Instruct |
|---|
| CommonSense reasoning | 2.277 / 1.151 | 1.891 / 0.934 |
| Maths | 1.060 / 0.124 | 1.075 / 0.151 |
| Paraphrasing | 3.518 / 1.308 | 3.536 / 1.348 |
| Reading comprehension | 2.966 / 1.111 | 2.599 / 1.331 |
| Summarization | 2.217 / 1.068 | 1.827 / 0.822 |
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:
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
@misc{gplsi-Aitana-2B-S-IP-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 Instruct IP: Instruction-tuned model for intellectual property 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-IP-Instruct}},
note = {Accessed: 2026-05-21}
}
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.
