gplsi

Aitana-7B-S-base

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

Model Description

Table with columns: Property, Value
Property	Value
Base Model	BSC-LT/salamandra-7b
Architecture	Transformer decoder-only
Parameters	~7.77B
Languages	Valencian, Spanish, English
License	Apache 2.0

Aitana-7B-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:

Table with columns: Dataset ID, Name, Language, Source
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

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
import torch
from transformers import pipeline, AutoTokenizer

model_id = "gplsi/Aitana-7B-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'])

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

Table with columns: Language, Salamandra-7B, Aitana-7B-S-base
Language	Salamandra-7B	Aitana-7B-S-base
Spanish	0.248	0.26
Catalan	0.364	0.373
English	0.319	0.349
Valencian	0.663	0.664

Valencian

Classification Benchmarks

Table with columns: Dataset, Lang., Task, Metric, Salamandra-7B, Aitana-7B-S-base
Dataset	Lang.	Task	Metric	Salamandra-7B	Aitana-7B-S-base
XNLI	va	Natural Language Inference	acc	0.496	0.495

Generation Benchmarks

Table with columns: Dataset, Lang., Task, Metric, Salamandra-7B, Aitana-7B-S-base
Dataset	Lang.	Task	Metric	Salamandra-7B	Aitana-7B-S-base
Cocoteros	va	Reading Comprehension	bleu	12.30	16.09
Phrases ca-va	va-ca	Translation - Adaptation	bleu	86.83	86.53
Phrases va-ca	va-ca

Catalan

Classification Benchmarks

Table with columns: Dataset, Lang., Task, Metric, Salamandra-7B, Aitana-7B-S-base
Dataset	Lang.	Task	Metric	Salamandra-7B	Aitana-7B-S-base
Belebele Cat_latn	ca	Reading Comprehension	acc	0.51	0.546
COPA	ca	Commonsense Reasoning	acc	0.798	0.812
XStoryCloze	ca

Generation Benchmarks

Table with columns: Dataset, Lang., Task, Metric, Salamandra-7B, Aitana-7B-S-base
Dataset	Lang.	Task	Metric	Salamandra-7B	Aitana-7B-S-base
Cabreu abstractive	ca	Summarization	bleu	8.46	11.34
Cabreu extractive	ca	Summarization	bleu	44.62	41.73
Cabreu extreme	ca

Spanish

Classification Benchmarks

Table with columns: Dataset, Lang., Task, Metric, Salamandra-7B, Aitana-7B-S-base
Dataset	Lang.	Task	Metric	Salamandra-7B	Aitana-7B-S-base
Belebele	es	Reading Comprehension	acc	0.49	0.55
PAWS	es	Paraphrasing	acc	0.616	0.591
XNLI	es

Generation Benchmarks

Table with columns: Dataset, Lang., Task, Metric, Salamandra-7B, Aitana-7B-S-base
Dataset	Lang.	Task	Metric	Salamandra-7B	Aitana-7B-S-base
Cocoteros	es	Reading Comprehension	bleu	14.57	17.35
XLSum	es	Summarization	bleu	3.52	5.79

English

Classification Benchmarks

Table with columns: Dataset, Lang., Task, Metric, Salamandra-7B, Aitana-7B-S-base
Dataset	Lang.	Task	Metric	Salamandra-7B	Aitana-7B-S-base
Arc Challenge	en	Question Answering	acc	0.53	0.529
Arc Easy	en	Question Answering	acc	0.822	0.816
Belebele	en

Additional Information

Author

The model has been developed by the Language and Information Systems Group (GPLSI), the Centro de Inteligencia Digital (CENID), and the [Language Modeling Group at Barcelona Supercomputing Center] (https://www.bsc.es/research-development/research-areas/cognitive-computing/language-modeling), all contributing to cutting-edge research in Natural Language Processing (NLP). GPLSI and CENID are part of the University of Alicante (UA), while the Language Modeling Group operates within the Barcelona Supercomputing Center.

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

bibtex
@misc{gplsi-Aitana-7B-S-base,
  author       = {Sepúlveda-Torres, Robiert and Baucells, Irene and Estevanell-Valladares, Ernesto L. and Galiano, Santiago and Consuegra-Ayala, Juan Pablo and Miró Maestre, María and Martínez-Murillo, Iván and Grande, Eduardo 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 7B base: Continually pre-trained on Valencian},
  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/gplsi/Aitana-2B-S-base}},
  note         = {Accessed: 2026-4-8}
}

Model provider

gplsi

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BSC-LT/salamandra-7b

Fine-tuned

this model

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

Model Description

Table with columns: Property, Value
Property	Value
Base Model	BSC-LT/salamandra-7b
Architecture	Transformer decoder-only
Parameters	~7.77B
Languages	Valencian, Spanish, English
License	Apache 2.0

Training Data

This model was trained on the following ALIA datasets:

Table with columns: Dataset ID, Name, Language, Source
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

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
import torch
from transformers import pipeline, AutoTokenizer

model_id = "gplsi/Aitana-7B-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'])

Evaluation

Normalized score per language

Table with columns: Language, Salamandra-7B, Aitana-7B-S-base
Language	Salamandra-7B	Aitana-7B-S-base
Spanish	0.248	0.26
Catalan	0.364	0.373
English	0.319	0.349
Valencian	0.663	0.664

Valencian

Classification Benchmarks

Table with columns: Dataset, Lang., Task, Metric, Salamandra-7B, Aitana-7B-S-base
Dataset	Lang.	Task	Metric	Salamandra-7B	Aitana-7B-S-base
XNLI	va	Natural Language Inference	acc	0.496	0.495

Generation Benchmarks

Table with columns: Dataset, Lang., Task, Metric, Salamandra-7B, Aitana-7B-S-base
Dataset	Lang.	Task	Metric	Salamandra-7B	Aitana-7B-S-base
Cocoteros	va	Reading Comprehension	bleu	12.30	16.09
Phrases ca-va	va-ca	Translation - Adaptation	bleu	86.83	86.53
Phrases va-ca	va-ca

Catalan

Classification Benchmarks

Table with columns: Dataset, Lang., Task, Metric, Salamandra-7B, Aitana-7B-S-base
Dataset	Lang.	Task	Metric	Salamandra-7B	Aitana-7B-S-base
Belebele Cat_latn	ca	Reading Comprehension	acc	0.51	0.546
COPA	ca	Commonsense Reasoning	acc	0.798	0.812
XStoryCloze	ca

Generation Benchmarks

Table with columns: Dataset, Lang., Task, Metric, Salamandra-7B, Aitana-7B-S-base
Dataset	Lang.	Task	Metric	Salamandra-7B	Aitana-7B-S-base
Cabreu abstractive	ca	Summarization	bleu	8.46	11.34
Cabreu extractive	ca	Summarization	bleu	44.62	41.73
Cabreu extreme	ca

Spanish

Classification Benchmarks

Table with columns: Dataset, Lang., Task, Metric, Salamandra-7B, Aitana-7B-S-base
Dataset	Lang.	Task	Metric	Salamandra-7B	Aitana-7B-S-base
Belebele	es	Reading Comprehension	acc	0.49	0.55
PAWS	es	Paraphrasing	acc	0.616	0.591
XNLI	es

Generation Benchmarks

Table with columns: Dataset, Lang., Task, Metric, Salamandra-7B, Aitana-7B-S-base
Dataset	Lang.	Task	Metric	Salamandra-7B	Aitana-7B-S-base
Cocoteros	es	Reading Comprehension	bleu	14.57	17.35
XLSum	es	Summarization	bleu	3.52	5.79

English

Classification Benchmarks

Table with columns: Dataset, Lang., Task, Metric, Salamandra-7B, Aitana-7B-S-base
Dataset	Lang.	Task	Metric	Salamandra-7B	Aitana-7B-S-base
Arc Challenge	en	Question Answering	acc	0.53	0.529
Arc Easy	en	Question Answering	acc	0.822	0.816
Belebele	en

Additional Information

Author

Funding

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:

License

Apache License, Version 2.0

Disclaimer

Reference

bibtex
@misc{gplsi-Aitana-7B-S-base,
  author       = {Sepúlveda-Torres, Robiert and Baucells, Irene and Estevanell-Valladares, Ernesto L. and Galiano, Santiago and Consuegra-Ayala, Juan Pablo and Miró Maestre, María and Martínez-Murillo, Iván and Grande, Eduardo 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 7B base: Continually pre-trained on Valencian},
  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/gplsi/Aitana-2B-S-base}},
  note         = {Accessed: 2026-4-8}
}

Aitana-7B-S-base

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Table of Contents

Model Description

Training Data

Data Sources

Intended Uses

How to Use

Transformers

Evaluation

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

Funding

Acknowledgments

License

Disclaimer

Reference

Explore FriendliAI today

Table of Contents

Model Description

Training Data

Data Sources

Intended Uses

How to Use

Transformers

Evaluation

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

Funding

Acknowledgments

License

Disclaimer

Reference