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---
license: apache-2.0
language:
- ca
- es
- en
base_model: BSC-LT/salamandra-7b
tags:
- valencian
- catalan
- spanish
- english
- text-generation
- alia
- gplsi
datasets:
- gplsi/alia_dogv
- gplsi/alia_les_corts
- gplsi/alia_amic
- gplsi/alia_boua
- gplsi/alia_tourism
library_name: transformers
pipeline_tag: text-generation
---
# Aitana-7B-S-base-1.0
**Aitana-7B-S-base-1.0** is a generative language model from the **Aitana family**, developed by the [GPLSI (Language and Information System Group)](https://gplsi.dlsi.ua.es/) at the University of Alicante. This model is based on [BSC-LT/salamandra-7b](https://huggingface.co/BSC-LT/salamandra-7b) 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](#model-description)
- [Evaluation](#evaluation)
- [Training Data](#training-data)
- [Intended Uses](#intended-uses)
- [How to Use](#how-to-use)
- [Additional Information](#additional-information)
## Model Description
| Property | Value |
|----------|-------|
| **Base Model** | [BSC-LT/salamandra-7b](https://huggingface.co/BSC-LT/salamandra-7b) |
| **Architecture** | Transformer decoder-only |
| **Parameters** | ~7.77B |
| **Languages** | Valencian, Spanish, English |
| **License** | Apache 2.0 |
Aitana-7B-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](https://huggingface.co/datasets/gplsi/alia_dogv) |
| dc9 | dogv_es_2025 | Spanish | [gplsi/alia_dogv](https://huggingface.co/datasets/gplsi/alia_dogv) |
| dc10 | corts_es_va_2025 | Spanish/Valencian | [gplsi/alia_les_corts](https://huggingface.co/datasets/gplsi/alia_les_corts) |
| dc11 | amic_va_2025 | Valencian | [gplsi/alia_amic](https://huggingface.co/datasets/gplsi/alia_amic) |
| dc12 | boua_va_2025 | Valencian | [gplsi/alia_boua](https://huggingface.co/datasets/gplsi/alia_boua) |
| dc13 | boua_es_2025 | Spanish | [gplsi/alia_boua](https://huggingface.co/datasets/gplsi/alia_boua) |
| dc14 | tourism_va_2025 | Valencian | [gplsi/alia_tourism](https://huggingface.co/datasets/gplsi/alia_tourism) |
| dc15 | tourism_es_2025 | Spanish | [gplsi/alia_tourism](https://huggingface.co/datasets/gplsi/alia_tourism) |
| dc16 | tourism_en_2025 | English | [gplsi/alia_tourism](https://huggingface.co/datasets/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
```python
import torch
from transformers import pipeline, AutoTokenizer
model_id = "gplsi/Aitana-7B-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'])
```
## Evaluation
In the following table, we can see the results obtained with different benchmarks from [lm-evaluation-harness](https://github.com/EleutherAI/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-7B | Aitana-7B-S-base-1.0 |
|----------|----------|----------|
| **Spanish** | **0.255** | 0.252 |
| **Catalan** | 0.373 | **0.378** |
| **English** | 0.329 | **0.364** |
| **Valencian** | **0.614** | **0.614** |
### Valencian
#### Classification Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-7B | Aitana-7B-S-base-1.0 |
|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|
| XNLI | va |Natural Language Inference | acc | **0.50** | 0.50 |
#### Generation Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-7B | Aitana-7B-S-base-1.0 |
|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|
| Cocoteros | va |Reading Comprehension | bleu | 12.01 | **16.19** |
| Phrases ca-va | va-ca |Translation - Adaptation | bleu | **86.80** | 85.33 |
| Phrases va-ca | va-ca |Translation - Adaptation | bleu | **94.71** | 80.00 |
| Phrases va-es | va-es |Translation | bleu | 79.74 | **80.59** |
| Phrases es-va | es-va |Translation | bleu | 66.42 | **69.78** |
| Truthfulqa_va | va | Truthfulness | bleu_acc| 0.33 | **0.37** |
### Catalan
#### Classification Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-7B | Aitana-7B-S-base-1.0 |
|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|
| Belebele Cat_latn | ca | Reading Comprehension | acc | 0.51 | **0.54** |
| COPA | ca | Commonsense Reasoning | acc | 0.80 | **0.82** |
| XStoryCloze | ca | Commonsense Reasoning | acc | 0.75 | **0.77** |
| OpenBookQA | ca | Question Answering | acc | 0.38 | **0.38** |
| PAWS | ca | Paraphrasing | acc | 0.62 | **0.62** |
| PiQA | ca | Question Answering | acc | 0.71 | **0.72** |
| SiQA | ca | Question Answering | acc | 0.49 | **0.51** |
| ARC Easy | ca | Question Answering | acc | 0.73 | **0.73** |
| ARC Challenge | ca | Question Answering | acc | **0.47** | 0.46 |
| XNLI | ca | Natural Language Inference | acc | **0.51** | 0.50 |
| Teca | ca | Natural Language Inference | acc | 0.53 | **0.53** |
| WNLI | ca | Natural Language Inference | acc | 0.59 | **0.62** |
| Catcola | ca | Linguistic Acceptability | acc | **0.73** | 0.73 |
| Catcola | ca | Linguistic Acceptability | mcc | **0.29** | 0.15 |
| Catalanqa | ca | Question Answering | F1 | 0.82 | **0.83** |
| Mgsm direct | ca | Math | exact match | 0.07 | **0.09** |
| Catalanqa | ca | Question Answering | exact match | 0.62 | **0.65** |
| Xquad | ca | Question Answering | exact match | 0.49 | **0.51** |
| Xquad | ca | Question Answering | F1 | 0.71 | **0.73** |
#### Generation Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-7B | Aitana-7B-S-base-1.0 |
|------------------------------|--------|----------------------------|--------|----------------|-----------------------|
| Cabreu abstractive | ca | Summarization | bleu | 8.73 | **11.32** |
| Cabreu extractive | ca | Summarization | bleu | **44.55** | 41.80 |
| Cabreu extreme | ca | Summarization | bleu | 10.66 | **12.54** |
### Spanish
#### Classification Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-7B | Aitana-7B-S-base-1.0 |
|------------------------------|--------|---------------------------|-------------|---------------|-----------------------|
| Belebele | es | Reading Comprehension | acc | 0.493 | **0.561** |
| PAWS | es | Paraphrasing | acc | **0.608** | 0.591 |
| XNLI | es | Natural Language Inference| acc | **0.468** | 0.462 |
| WNLI | es | Natural Language Inference| acc | **0.465** | 0.437 |
| XStoryCloze | es | Commonsense Reasoning | acc | 0.745 | **0.756** |
| Escola | es | Linguistic Acceptability | acc | **0.706** | 0.678 |
| Escola | es | Linguistic Acceptability | mcc | **0.295** | 0.146 |
| OpenbookQA | es | Question Answering | acc | **0.406** | 0.382 |
| MGSM Direct | es | Math | exact match | 0.068 | **0.080** |
| XQUAD | es | Question Answering | exact match | 0.501 | **0.505** |
| XQUAD | es | Question Answering | F1 | 0.711 | **0.719** |
#### Generation Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-7B | Aitana-7B-S-base-1.0 |
|------------------------------|--------|---------------------|---------|----------------|-----------------------|
| Cocoteros | es |Reading Comprehension| bleu | 13.68 | **17.51** |
| XLSum | es | Summarization | bleu | 3.59 | **5.75** |
### English
#### Classification Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-7B | Aitana-7B-S-base-1.0 |
|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|
| Arc Challenge | en | Question Answering | acc | **0.527** | 0.526 |
| Arc Easy | en | Question Answering | acc | **0.824** | 0.814 |
| Belebele | en | Reading Comprehension | acc | 0.549 | **0.573** |
| PAWS | en | Paraphrasing | acc | **0.633** | 0.615 |
| XNLI | en | Natural Language Inference | acc | **0.483** | 0.476 |
| XStoryCloze | en | Commonsense Reasoning | acc | **0.795** | 0.793 |
| OpenBookQA | en | Question Answering | acc | 0.356 | **0.362** |
| PiQA | en | Question Answering | acc | 0.797 | **0.799** |
| Social iqa | en | Question Answering | acc | **0.513** | 0.512 |
| WNLI | en | Natural Language Inference | acc | 0.479 | **0.606** |
| MGSM Direct | en | Math | exact match | 0.280 | **0.564** |
| TriviaQA | en | Question Answering | exact match | 0.597 | **0.602** |
| CoLA | en | Linguistic Acceptability | mcc | **0.412** | 0.361 |
## Additional Information
### Author
The model has been developed by the **Language and [Information Systems Group (GPLSI)](https://gplsi.dlsi.ua.es/)** and the **[Centro de Inteligencia Digital (CENID)](https://cenid.es)**, both part of the **[University of Alicante (UA)](https://www.ua.es/es/)**, 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-2B-S](https://huggingface.co/gplsi/Aitana-2B-S) - Valencian-focused 2B model
- [gplsi/Aitana-2B-S-base-1.0](https://huggingface.co/gplsi/Aitana-2B-S-base-1.0) - Base version (1.0) of the 2B model
- [gplsi/Aitana-6.3B](https://huggingface.co/gplsi/Aitana-6.3B) - Larger 6.3B parameter model
- [gplsi/Aitana-TA-2B-S](https://huggingface.co/gplsi/Aitana-TA-2B-S) - Translation model (Spanish ↔ Valencian)
- [gplsi/Aitana-2B-S-LF](https://www.google.com/search?q=https://huggingface.co/gplsi/Aitana-2B-S-LF) - 2B Text Generation variant
- [gplsi/Aitana-2B-S-tourism-base-1.0](https://huggingface.co/gplsi/Aitana-2B-S-tourism-base-1.0) - Domain-specific base model focused on Tourism
- [gplsi/Aitana-tourism-mb-encoder-1.0](https://huggingface.co/gplsi/Aitana-tourism-mb-encoder-1.0) - Tourism domain Fill-Mask/Encoder model
- [gplsi/Aitana-FraudDetection-R-1.0](https://huggingface.co/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](https://www.bsc.es/es/discover-bsc/organisation/research-structure/language-technologies-laboratory)
- [Centro Vasco de Tecnología de la Lengua (HiTZ)](https://www.hitz.eus/es)
- [Centro Singular de Investigación en Tecnologías Inteligentes (CiTIUS)](https://citius.gal/)
- [Sistemas Inteligentes de Acceso a la Información (SINAI)](https://www.ujaen.es/investigacion-y-transferencia/grupos-de-investigacion/sistemas-inteligentes-de-acceso-la-informacion-sinai)
- [Instituto Universitario de Investigación Informática (IUII)](https://web.ua.es/es/iuii/)
- [Leonardo HPC System](https://leonardo-supercomputer.cineca.eu/)
- [European supercomputing ecosystem (EUROHPC)](https://www.eurohpc-ju.europa.eu/)
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](https://www.apache.org/licenses/LICENSE-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-2B-S-base-1.0,
author = {Estevanell-Valladares, Ernesto L. and Yáñez-Romero, Fabio and Sepúlveda-Torres, Robiert and Consuegra-Ayala, Juan Pablo and Galiano, 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 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-1.0}},
note = {Accessed: 2026-4-8}
}
```
---
**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.**

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