license, language, base_model, tags, datasets, library_name, pipeline_tag

license	language	base_model	tags	datasets	library_name	pipeline_tag
apache-2.0	ca es en	gplsi/Aitana-7B-S-base-1.0	valencian spanish english text-generation instruct alia gplsi	OpenAssistant/oasst2 OpenAssistant/oasst1 BSC-LT/m-personas projecte-aina/RAG_Multilingual facebook/flores CohereLabs/aya_dataset Unbabel/TowerBlocks-v0.2 projecte-aina/MentorES databricks/databricks-dolly-15k tatsu-lab/alpaca openai/gsm8k Open-Orca/OpenOrca HuggingFaceH4/no_robots projecte-aina/CoQCat gplsi/boua_parallel allenai/scifact somosnlp/LingComp_QA somosnlp/instruct-legal-refugiados-es	transformers	text-generation

Aitana-7B-S-Instruct-v0.1

Aitana-7B-S-Instruct-v0.1 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-7B-S-base-1.0, this model has been fine-tuned to follow instructions effectively across Valencian, Spanish, and English, with particular emphasis on enhancing Valencian language capabilities.

Table of Contents

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

Model Description

Property	Value
Base Model	gplsi/Aitana-7B-S-base-1.0
Architecture	Transformer decoder-only
Parameters	~7.77B
Languages	Valencian, Spanish, English
License	Apache 2.0

Aitana-7B-S-Instruct-v0.1 is an instruction-tuned variant of Aitana-7B-S-base-1.0, fine-tuned on multilingual instruction data to follow user prompts and generate helpful responses across Valencian, Spanish, and English.

Training Data

This model was instruction fine-tuned on the ALIA Instruction/v12 dataset, composed of the following sources:

Dataset ID	Name	Languages	Source
ins1	OpenAssistant2 (OASST2)	CA, EN, ES, VA	OpenAssistant/oasst2
ins2	OpenAssistant1 (OASST1)	CA, VA	OpenAssistant/oasst1
ins3	M-Personas	CA, EN, ES, VA	BSC-LT/m-personas
ins4	RAG Multilingual	CA, EN, ES, VA	projecte-aina/RAG_Multilingual
ins5	FLORES	CA, EN, ES	facebook/flores
ins6	Aya Dataset	EN, ES, VA	CohereLabs/aya_dataset
ins7	TowerBlocks	EN, ES	Unbabel/TowerBlocks-v0.2
ins8	Mentor / Mentores	CA, ES, VA	projecte-aina/MentorES / projecte-aina/MentorCA
ins9	Dolly / Dolly 3K	CA, EN, VA	databricks/databricks-dolly-15k
ins10	Alpaca	EN, VA	tatsu-lab/alpaca
ins11	GSM8K	EN, VA	openai/gsm8k
ins12	OpenOrca	EN	Open-Orca/OpenOrca
ins13	No Robots	EN	HuggingFaceH4/no_robots
ins14	CoQCA / CoQCat	CA, VA	projecte-aina/CoQCat
ins15	BOUA	ES	gplsi/boua_parallel
ins16	SciFact	VA	allenai/scifact
ins17	LingComp QA	VA	somosnlp/LingComp_QA
ins18	Instruct Legal Refugiados	VA	somosnlp/instruct-legal-refugiados-es
ins19	Amic-Paralelo	ES	—

The model was NOT instruction-tuned on Catalan data, though some Catalan appears in multilingual datasets.

Intended Uses

This model can be used for:

• Instruction following in Valencian, Spanish, and English
• Chat and conversational applications requiring multilingual support
• Text generation with task-specific prompting
• Domain-specific applications in administrative, legal, or tourism contexts

Note

: As an instruction-tuned model, it is designed to follow user prompts and generate helpful responses. It is not intended for use as a factual knowledge base.

How to Use

Transformers

import torch
from transformers import pipeline, AutoTokenizer

model_id = "gplsi/Aitana-7B-S-Instruct-v0.1"
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 = "Explica què són les Corts Valencianes i quina funció tenen."
result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
print(result[0]['generated_text'])
# Spanish example
text = "Describe las principales funciones del gobierno autonómico valenciano."
result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
print(result[0]['generated_text'])
# English example
text = "Explain the role of tourism in the Valencian Community economy."
result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
print(result[0]['generated_text'])

Evaluation

In the following table, we present the results obtained with different benchmarks from lm-evaluation-harness in comparison with Salamandra-7B-Instruct. The results reflect the instruction-tuned performance of both models.

Normalized score per language

Language	Salamandra-7B-Instruct	Aitana-7B-S-Instruct-v0.1
Spanish	0.236	0.219
Catalan	0.343	0.304
English	0.300	0.303
Valencian	0.546	0.600

Valencian

Classification Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-7B-Instruct	Aitana-7B-S-Instruct-v0.1
XNLI	va	Natural Language Inference	acc	0.552	0.534

Generation Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-7B-Instruct	Aitana-7B-S-Instruct-v0.1
Cocoteros	va	Reading Comprehension	bleu	6.391	8.929
Phrases ca-va	va-ca	Translation - Adaptation	bleu	67.980	81.743
Phrases va-ca	va-ca	Translation - Adaptation	bleu	79.375	83.501
Phrases va-es	va-es	Translation	bleu	63.104	80.329
Phrases es-va	es-va	Translation	bleu	51.64	63.95
Truthfulqa_va	va	Truthfulness	bleu_acc	0.454	0.412

Catalan

Classification Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-7B-Instruct	Aitana-7B-S-Instruct-v0.1
Belebele Cat_latn	ca	Reading Comprehension	acc	0.718	0.581
COPA	ca	Commonsense Reasoning	acc	0.824	0.822
XStoryCloze	ca	Commonsense Reasoning	acc	0.708	0.678
OpenBookQA	ca	Question Answering	acc	0.374	0.36
PAWS	ca	Paraphrasing	acc	0.671	0.662
PiQA	ca	Question Answering	acc	0.718	0.722
ARC Easy	ca	Question Answering	acc	0.686	0.713
ARC Challenge	ca	Question Answering	acc	0.425	0.435
XNLI	ca	Natural Language Inference	acc	0.559	0.540
Teca	ca	Natural Language Inference	acc	0.557	0.522
WNLI	ca	Natural Language Inference	acc	0.592	0.479
Catcola	ca	Linguistic Acceptability	acc	0.660	0.687
Catcola	ca	Linguistic Acceptability	mcc	0.170	0.156
Catalanqa	ca	Question Answering	F1	0.576	0.526
Mgsm direct	ca	Math	exact match	0.02	0.004
Catalanqa	ca	Question Answering	exact match	0.259	0.176
Xquad	ca	Question Answering	exact match	0.228	0.157
Xquad	ca	Question Answering	F1	0.507	0.451

Generation Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-7B-Instruct	Aitana-7B-S-Instruct-v0.1
Cabreu abstractive	ca	Summarization	bleu	8.60	10.10
Cabreu extractive	ca	Summarization	bleu	39.10	28.37
Cabreu extreme	ca	Summarization	bleu	3.21	3.86

Spanish

Classification Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-7B-Instruct	Aitana-7B-S-Instruct-v0.1
Belebele	es	Reading Comprehension	acc	0.698	0.590
PAWS	es	Paraphrasing	acc	0.629	0.626
XNLI	es	Natural Language Inference	acc	0.487	0.485
WNLI	es	Natural Language Inference	acc	0.549	0.493
XStoryCloze	es	Commonsense Reasoning	acc	0.674	0.676
Escola	es	Linguistic Acceptability	acc	0.577	0.681
Escola	es	Linguistic Acceptability	mcc	0.179	0.178
OpenbookQA	es	Question Answering	acc	0.374	0.392
MGSM Direct	es	Math	exact match	0.100	0.100
XQUAD	es	Question Answering	exact match	0.189	0.087
XQUAD	es	Question Answering	F1	0.467	0.413

Generation Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-7B-Instruct	Aitana-7B-S-Instruct-v0.1
Cocoteros	es	Reading Comprehension	bleu	6.306	8.680
XLSum	es	Summarization	bleu	2.048	1.502

English

Classification Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-7B-Instruct	Aitana-7B-S-Instruct-v0.1
Arc Challenge	en	Question Answering	acc	0.478	0.523
Arc Easy	en	Question Answering	acc	0.780	0.811
Belebele	en	Reading Comprehension	acc	0.769	0.622
PAWS	en	Paraphrasing	acc	0.655	0.677
XNLI	en	Natural Language Inference	acc	0.534	0.555
XStoryCloze	en	Commonsense Reasoning	acc	0.729	0.716
OpenBookQA	en	Question Answering	acc	0.348	0.340
PiQA	en	Question Answering	acc	0.781	0.784
Social iqa	en	Question Answering	acc	0.520	0.524
WNLI	en	Natural Language Inference	acc	0.493	0.493
MGSM Direct	en	Math	exact match	0.080	0.200
TriviaQA	en	Question Answering	exact match	0.204	0.433

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 Aitana-7B-S-Instruct-v0.1 against Salamandra-7B-Instruct.

Task Category	Salamandra-7B-Instruct	Aitana-7B-S-Instruct-v0.1
CommonSense reasoning	2.637 / 1.295	2.989 / 1.200
Maths	2.386 / 1.536	2.584 / 1.474
Paraphrasing	3.725 / 0.967	3.927 / 0.981
Reading comprehension	3.472 / 1.015	3.420 / 1.268
Summarization	2.369 / 0.932	1.862 / 0.713
Translation	3.770 / 0.580	3.895 / 0.814
Overall Avg	3.060 / 1.054	3.113 / 1.075

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 developed by the GPLSI research group, which includes:

• gplsi/Aitana-7B-S-base-1.0 - Base version (1.0) of the 7B model
• gplsi/Aitana-7B-S-Instruct-v0.1 - Instruction-tuned 7B model
• gplsi/Aitana-2B-S - Valencian-focused 2B model
• gplsi/Aitana-2B-S-base-1.0 - Base version (1.0) of the 2B model
• gplsi/Aitana-2B-S-Instruct-v0.1 - Instruction-tuned 2B model
• gplsi/Aitana-2B-S-Instruct-Aligned-v0.1 - DPO-aligned instruction-tuned 2B model
• gplsi/Aitana-6.3B - Larger 6.3B parameter model
• gplsi/Aitana-TA-2B-S - Translation model (Spanish ↔ Valencian)
• gplsi/Aitana-2B-S-LF - 2B Text Generation variant
• gplsi/Aitana-2B-S-tourism-base-1.0 - Domain-specific base model focused on Tourism
• gplsi/Aitana-tourism-mb-encoder-1.0 - Tourism domain Fill-Mask/Encoder model
• gplsi/Aitana-FraudDetection-R-1.0 - Text Classification model for Fraud Detection

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-7B-S-Instruct-v0.1,
  author       = {Sepúlveda-Torres, Robiert and Martínez-Murillo, Iván 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 7B Instruct: Instruction-tuned model for 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-7B-S-Instruct-v0.1}},
  note         = {Accessed: 2026-05-11}
}

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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.