--- 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 **Aitana-7B-S-base** 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 and [Language Modeling Group at Barcelona Supercomputing Center](https://www.bsc.es/research-development/research-areas/cognitive-computing/language-modeling). 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 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) | |-|alia_multilingual_parallel_sentences|Spanish/Valencian/English|[gplsi/alia_multilingual_parallel_sentences](https://huggingface.co/datasets/gplsi/alia_multilingual_parallel_sentences)| ### 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](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 | |----------|----------|----------| | **Spanish** | 0.248 | **0.26** | | **Catalan** | 0.364 | **0.373** | | **English** | 0.319 | **0.349** | | **Valencian** | 0.663 | **0.664** | ### Valencian #### Classification Benchmarks | Dataset | Lang. | Task | Metric | Salamandra-7B | Aitana-7B-S-base | |------------------------------|--------|----------------------------|-------------|---------------|-----------------------| | XNLI | va |Natural Language Inference | acc | **0.496** | 0.495 | #### Generation Benchmarks | 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 |Translation - Adaptation | bleu | **94.68** | 82.99 | | Phrases va-es | va-es |Translation | bleu | 79.83 | **80.76** | | Phrases es-va | es-va |Translation | bleu | 66.31 | **71.01** | | Truthfulqa_va | va | Truthfulness | bleu_acc| 0.353 | **0.388** | ### Catalan #### Classification Benchmarks | 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 | Commonsense Reasoning | acc | 0.75 | **0.767** | | OpenBookQA | ca | Question Answering | acc | 0.366 | **0.376** | | PAWS | ca | Paraphrasing | acc | **0.626** | 0.613 | | PiQA | ca | Question Answering | acc | 0.702 | **0.725** | | SiQA | ca | Question Answering | acc | 0.489 | **0.506** | | ARC Easy | ca | Question Answering | acc | 0.726 | **0.73** | | ARC Challenge | ca | Question Answering | acc | **0.47** | 0.459 | | XNLI | ca | Natural Language Inference | acc | **0.504** | 0.494 | | Teca | ca | Natural Language Inference | acc | **0.527** | 0.514. | | WNLI | ca | Natural Language Inference | acc | 0.577 | **0.633** | | Catcola | ca | Linguistic Acceptability | acc | **0.732** | 0.71 | Catalanqa | ca | Question Answering | F1 | **0.832** | 0.829 | | Catalanqa | ca | Question Answering | exact match | 0.62 | **0.65** | | Mgsm direct | ca | Math | exact match | 0.068 | **0.096** | | Xquad | ca | Question Answering | exact match | **0.498** | 0.497 | | Xquad | ca | Question Answering | F1 | 0.717 | **0.724** | #### Generation Benchmarks | 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 | Summarization | bleu | 11.02 | **12.44** | ### Spanish #### Classification Benchmarks | 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 | Natural Language Inference| acc | **0.462** | 0.447 | | WNLI | es | Natural Language Inference| acc | **0.45** | **0.45** | | XStoryCloze | es | Commonsense Reasoning | acc | 0.746 | **0.754** | | Escola | es | Linguistic Acceptability | acc | - | - | | Escola | es | Linguistic Acceptability | mcc | - | - | | OpenbookQA | es | Question Answering | acc | - | - | | MGSM Direct | es | Math | exact match | 0.064 | **0.084** | | XQUAD | es | Question Answering | exact match | **0.51** | 0.509 | | XQUAD | es | Question Answering | F1 | 0.746 | **0.754** | #### Generation Benchmarks | 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 | 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 | Reading Comprehension | acc | **0.562** | 0.537 | | PAWS | en | Paraphrasing | acc | **0.632** | 0.604 | | XNLI | en | Natural Language Inference | acc | **0.474** | 0.472 | | XStoryCloze | en | Commonsense Reasoning | acc | **0.796** | 0.79 | | OpenBookQA | en | Question Answering | acc | 0.352 | **0.356** | | PiQA | en | Question Answering | acc | 0.793 | **0.796** | | Social iqa | en | Question Answering | acc | **0.509** | 0.508 | | WNLI | en | Natural Language Inference | acc | 0.464 | **0.549** | | MGSM Direct | en | Math | exact match | 0.264 | **0.564** | | TriviaQA | en | Question Answering | exact match | 0.597 | **0.601** | | CoLA | en | Linguistic Acceptability | mcc | **0.381** | 0.339 | ## Additional Information ### Author The model has been developed by the **Language and [Information Systems Group (GPLSI)](https://gplsi.dlsi.ua.es/)**, the **[Centro de Inteligencia Digital (CENID)](https://cenid.es)**, 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)](https://www.ua.es/es/)**, while the Language Modeling Group operates within the [Barcelona Supercomputing Center](https://www.bsc.es/es). ### 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: - [Language Modeling at Barcelona Supercomputing Center](https://www.bsc.es/research-development/research-areas/cognitive-computing/language-modeling) - [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-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} } ``` --- **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.**