--- license: apache-2.0 language: - ca - es - en base_model: BSC-LT/salamandra-2b 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-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)](https://gplsi.dlsi.ua.es/) at the University of Alicante. This model is based on [BSC-LT/salamandra-2b](https://huggingface.co/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](#model-description) - [Evaluation](#evaluation) - [Training Data](#training-data) - [Intended Uses](#intended-uses) - [How to Use](#how-to-use) - [GGUF for LM Studio](#gguf-for-lm-studio) - [Additional Information](#additional-information) ## Model Description | Property | Value | |----------|-------| | **Base Model** | [BSC-LT/salamandra-2b](https://huggingface.co/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](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-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](https://lmstudio.ai/), [Ollama](https://ollama.ai/), 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 ```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](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 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)](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, which includes: - [gplsi/Aitana-2B-S](https://huggingface.co/gplsi/Aitana-2B-S) - Valencian-focused base model - [gplsi/Aitana-TA-2B-S](https://huggingface.co/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](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 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} } ``` --- **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.**