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---
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.**

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