Aitana-2B-S-base-1.0

Aitana-2B-S-base-1.0 is a generative language model from the Aitana family, developed by the GPLSI (Language and Information System Group) at the University of Alicante. This model is based on BSC-LT/salamandra-2b and has been continuously pre-trained on multilingual data (Valencian, Spanish, and English) to improve representation of Valencian and Catalan languages.

Table of Contents

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

Model Description

Property	Value
Base Model	BSC-LT/salamandra-2b
Architecture	Transformer decoder-only
Parameters	~2.25B
Languages	Valencian, Spanish, English
License	Apache 2.0

Aitana-2B-S-base-1.0 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:

Dataset ID	Name	Language	Source
dc8	dogv_va_2025	Valencian	gplsi/alia_dogv
dc9	dogv_es_2025	Spanish	gplsi/alia_dogv
dc10	corts_es_va_2025	Spanish/Valencian	gplsi/alia_les_corts
dc11	amic_va_2025	Valencian	gplsi/alia_amic
dc12	boua_va_2025	Valencian	gplsi/alia_boua
dc13	boua_es_2025	Spanish	gplsi/alia_boua
dc14	tourism_va_2025	Valencian	gplsi/alia_tourism
dc15	tourism_es_2025	Spanish	gplsi/alia_tourism
dc16	tourism_en_2025	English	gplsi/alia_tourism

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

import torch
from transformers import pipeline, AutoTokenizer

model_id = "gplsi/Aitana-2B-S-base-1.0"
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.

File	Quantization	Size	Quality
Aitana-s2b-c0dc17-Q4_K_M.gguf	Q4_K_M	~1.3 GB	Good balance
Aitana-s2b-c0dc17-f16.gguf	F16	~4.5 GB	Full precision

Using with llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="gplsi/Aitana-2B-S-base-1.0",
    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

Language	Salamandra 2B	Aitana-2B-S-base-1.0
Spanish	0.150	0.163
Catalan	0.224	0.220
English	0.168	0.161
Valencian	0.603	0.608

Valencian

Classification Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-2B	Aitana-2B-S-base-1.0
XNLI	va	Natural Language Inference	acc	0.475	0.474

Generation Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-2B	Aitana-2B-S-base-1.0
Cocoteros	va	Reading Comprehension	bleu	6.32	6.61
Phrases ca-va	va-ca	Translation - Adaptation	bleu	79.82	81.57
Phrases va-ca	va-ca	Translation - Adaptation	bleu	78.05	75.68
Phrases va-es	va-es	Translation	bleu	76.04	76.31
Phrases es-va	es-va	Translation	bleu	58.86	62.86

Catalan

Classification Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-2B	Aitana-2B-S-base-1.0
Belebele Cat_latn	ca	Reading Comprehension	acc	0.231	0.257
COPA	ca	Commonsense Reasoning	acc	0.700	0.690
XStoryCloze	ca	Commonsense Reasoning	acc	0.655	0.655
OpenBookQA	ca	Question Answering	acc	0.294	0.300
PAWS	ca	Paraphrasing	acc	0.556	0.566
PiQA	ca	Question Answering	acc	0.643	0.641
SiQA	ca	Question Answering	acc	0.434	0.425
ARC Easy	ca	Question Answering	acc	0.551	0.553
ARC Challenge	ca	Question Answering	acc	0.290	0.282
XNLI	ca	Natural Language Inference	acc	0.473	0.469
Teca	ca	Natural Language Inference	acc	0.465	0.430
WNLI	ca	Natural Language Inference	acc	0.577	0.577
Catcola	ca	Linguistic Acceptability	acc	0.543	0.596
Catcola	ca	Linguistic Acceptability	mcc	0.046	-0.002
Catalanqa	ca	Question Answering	F1	0.668	0.643
Mgsm direct	ca	Math	exact match	0.024	0.024
Catalanqa	ca	Question Answering	exact match	0.437	0.405
Xquad	ca	Question Answering	exact match	0.371	0.344
Xquad	ca	Question Answering	F1	0.579	0.568

Generation Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-2B	Aitana-2B-S-base-1.0
Cabreu abstractive	ca	Summarization	bleu	5.78	6.52
Cabreu extractive	ca	Summarization	bleu	42.89	41.61
Cabreu extreme	ca	Summarization	bleu	3.29	3.01

Spanish

Classification Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-2B	Aitana-2B-S-base-1.0
Belebele	es	Reading Comprehension	acc	0.228	0.263
PAWS	es	Paraphrasing	acc	0.561	0.553
XNLI	es	Natural Language Inference	acc	0.439	0.422
WNLI	es	Natural Language Inference	acc	0.563	0.563
XStoryCloze	es	Commonsense Reasoning	acc	0.653	0.655
Escola	es	Linguistic Acceptability	acc	0.593	0.618
Escola	es	Linguistic Acceptability	mcc	0.031	-0.020
OpenbookQA	es	Question Answering	acc	0.308	0.316
MGSM Direct	es	Math	exact match	0.020	0.032
XQUAD	es	Question Answering	exact match	0.377	0.341
XQUAD	es	Question Answering	F1	0.584	0.559

Generation Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-2B	Aitana-2B-S-base-1.0
Cocoteros	es	Reading Comprehension	bleu	8.46	7.043
XLSum	es	Summarization	bleu	0.801	1.622

English

Classification Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-2B	Aitana-2B-S-base-1.0
Arc Challenge	en	Question Answering	acc	0.370	0.360
Arc Easy	en	Question Answering	acc	0.722	0.712
Belebele	en	Reading Comprehension	acc	0.216	0.252
PAWS	en	Paraphrasing	acc	0.561	0.574
XNLI	en	Natural Language Inference	acc	0.462	0.452
XStoryCloze	en	Commonsense Reasoning	acc	0.711	0.713
OpenBookQA	en	Question Answering	acc	0.300	0.270
PiQA	en	Question Answering	acc	0.737	0.742
Social iqa	en	Question Answering	acc	0.454	0.450
WNLI	en	Natural Language Inference	acc	0.465	0.380
MGSM Direct	en	Math	exact match	0.064	0.06
TriviaQA	en	Question Answering	exact match	0.376	0.352

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.

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

@misc{gplsi-aitana-2B-S-base-1.0,
  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-1.0}},
  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.