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---
license: apache-2.0
language:
- ca
- es
- en
tags:
- valencian
- spanish
- english
- text-generation
- instruct
- alia
- gplsi
datasets:
- 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
library_name: transformers
pipeline_tag: text-generation
---
# Aitana-2B-SI-Instruct
**Aitana-2B-SI-Instruct** is an instruction-tuned generative language model from the **Aitana family**, developed by the [GPLSI (Language and Information Systems Group)](https://gplsi.dlsi.ua.es/) at the University of Alicante. Built on [gplsi/Aitana-2B-S-base-1.0](https://huggingface.co/gplsi/Aitana-2B-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](#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** | [gplsi/Aitana-2B-S-base-1.0](https://huggingface.co/gplsi/Aitana-2B-S-base-1.0) |
| **Architecture** | Transformer decoder-only |
| **Parameters** | ~2.25B |
| **Languages** | Valencian, Spanish, English |
| **License** | Apache 2.0 |
Aitana-2B-SI-Instruct is an instruction-tuned variant of Aitana-2B-S-base-1.0, fine-tuned on multilingual instruction data to follow user prompts and generate helpful responses across Valencian, Spanish, and English. The model was NOT instruction-tuned on Catalan data, though it retains some Catalan capabilities from its base model.
## 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](https://huggingface.co/datasets/OpenAssistant/oasst2) |
| ins2 | OpenAssistant1 (OASST1) | CA, VA | [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) |
| ins3 | M-Personas | CA, EN, ES, VA | [BSC-LT/m-personas](https://huggingface.co/datasets/BSC-LT/m-personas) |
| ins4 | RAG Multilingual | CA, EN, ES, VA | [projecte-aina/RAG_Multilingual](https://huggingface.co/datasets/projecte-aina/RAG_Multilingual) |
| ins5 | FLORES | CA, EN, ES | [facebook/flores](https://huggingface.co/datasets/facebook/flores) |
| ins6 | Aya Dataset | EN, ES, VA | [CohereLabs/aya_dataset](https://huggingface.co/datasets/CohereLabs/aya_dataset) |
| ins7 | TowerBlocks | EN, ES | [Unbabel/TowerBlocks-v0.2](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2) |
| ins8 | Mentor / Mentores | CA, ES, VA | [projecte-aina/MentorES](https://huggingface.co/datasets/projecte-aina/MentorES) / [projecte-aina/MentorCA](https://huggingface.co/datasets/projecte-aina/MentorCA) |
| ins9 | Dolly / Dolly 3K | CA, EN, VA | [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) |
| ins10 | Alpaca | EN, VA | [tatsu-lab/alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) |
| ins11 | GSM8K | EN, VA | [openai/gsm8k](https://huggingface.co/datasets/openai/gsm8k) |
| ins12 | OpenOrca | EN | [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) |
| ins13 | No Robots | EN | [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) |
| ins14 | CoQCA / CoQCat | CA, VA | [projecte-aina/CoQCat](https://huggingface.co/datasets/projecte-aina/CoQCat) |
| ins15 | BOUA | ES | [gplsi/boua_parallel](https://huggingface.co/datasets/gplsi/boua_parallel) |
| ins16 | SciFact | VA | [allenai/scifact](https://huggingface.co/datasets/allenai/scifact) |
| ins17 | LingComp QA | VA | [somosnlp/LingComp_QA](https://huggingface.co/datasets/somosnlp/LingComp_QA) |
| ins18 | Instruct Legal Refugiados | VA | [somosnlp/instruct-legal-refugiados-es](https://huggingface.co/datasets/somosnlp/instruct-legal-refugiados-es) |
| ins19 | Amic-Paralelo | ES | [gplsi/amic_parallel](https://huggingface.co/datasets/gplsi/amic_parallel) |
Catalan data was removed from the instruction tuning to focus on Valencian, Spanish, and English, 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
```python
import torch
from transformers import pipeline, AutoTokenizer
model_id = "gplsi/Aitana-2B-SI-Instruct"
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 tables, we present the results obtained with different benchmarks from [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) in comparison with [Salamandra-2B-Instruct](https://huggingface.co/BSC-LT/Salamandra-2B-Instruct). The results reflect the instruction-tuned performance of both models.
### Valencian
#### Classification Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-2B-Instruct | Aitana-2B-S-Instruct |
|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|
| XNLI | va |Natural Language Inference | acc | **0.520** | 0.508 |
#### Generation Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-2B-Instruct | Aitana-2B-S-Instruct |
|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|
| Cocoteros | va |Reading Comprehension | bleu | 2.796 | **3.121** |
| Phrases ca-va | va-ca |Translation - Adaptation | bleu | 58.425 | **76.427** |
| Phrases va-ca | va-ca |Translation - Adaptation | bleu | **70.660** | 68.902 |
| Phrases va-es | va-es |Translation | bleu | 65.427 | **69.694** |
| Phrases es-va | es-va |Translation | bleu | 45.688 | **55.725** |
| Truthfulqa_va | va | Truthfulness | bleu_acc | 0.409 | **0.415** |
### Catalan
#### Classification Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-2B-Instruct | Aitana-2B-S-Instruct |
|------------------------------|--------|---------------------------|-------------|---------------|-----------------------|
| Belebele Cat_latn | ca | Reading Comprehension | acc | **0.287** | 0.274 |
| COPA | ca | Commonsense Reasoning | acc | **0.708** | 0.704 |
| XStoryCloze | ca | Commonsense Reasoning | acc | **0.616** | 0.615 |
| OpenBookQA | ca | Question Answering | acc | 0.296 | **0.300** |
| PAWS | ca | Paraphrasing | acc | 0.602 | **0.608** |
| PiQA | ca | Question Answering | acc | 0.638 | **0.655** |
| SiQA | ca | Question Answering | acc | **0.422** | 0.419 |
| ARC Easy | ca | Question Answering | acc | 0.516 | **0.527** |
| ARC Challenge | ca | Question Answering | acc | 0.298 | **0.305** |
| XNLI | ca | Natural Language Inference| acc | 0.513 | **0.516** |
| Teca | ca | Natural Language Inference| acc | 0.486 | **0.499** |
| WNLI | ca | Natural Language Inference| acc | **0.563** | 0.451 |
| Catcola | ca | Linguistic Acceptability | acc | 0.492 | **0.585** |
| Catcola | ca | Linguistic Acceptability | mcc | **0.097** | -0.042 |
| Catalanqa | ca | Question Answering | F1 | **0.516** | 0.397 |
| Mgsm direct | ca | Math | exact match | 0.000 | **0.004** |
| Catalanqa | ca | Question Answering | exact match | **0.182** | 0.029 |
| Xquad | ca | Question Answering | exact match | **0.103** | 0.030 |
| Xquad | ca | Question Answering | F1 | **0.394** | 0.303 |
#### Generation Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-2B-Instruct | Aitana-2B-S-Instruct |
|------------------------------|--------|--------------------------|--------|----------------|-----------------------|
| Cabreu abstractive | ca | Summarization | bleu | 7.610 | **9.199** |
| Cabreu extractive | ca | Summarization | bleu | **38.002** | 14.869 |
| Cabreu extreme | ca | Summarization | bleu | 2.733 | **4.209** |
### Spanish
#### Classification Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-2B-Instruct | Aitana-2B-S-Instruct |
|------------------------------|--------|---------------------------|-------------|---------------|-----------------------|
| Belebele | es | Reading Comprehension | acc | **0.268** | 0.260 |
| PAWS | es | Paraphrasing | acc | 0.566 | **0.622** |
| XNLI | es | Natural Language Inference| acc | **0.463** | 0.419 |
| WNLI | es | Natural Language Inference| acc | 0.479 | **0.549** |
| XStoryCloze | es | Commonsense Reasoning | acc | 0.617 | **0.619** |
| Escola | es | Linguistic Acceptability | acc | 0.293 | **0.655** |
| Escola | es | Linguistic Acceptability | mcc | 0.020 | **0.087** |
| OpenbookQA | es | Question Answering | acc | 0.286 | **0.320** |
| MGSM Direct | es | Math | exact match | 0.020 | **0.032** |
| XQUAD | es | Question Answering | exact match | **0.066** | 0.039 |
| XQUAD | es | Question Answering | F1 | **0.355** | 0.305 |
#### Generation Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-2B-Instruct | Aitana-2B-S-Instruct |
|------------------------------|--------|---------------------|---------|----------------|-----------------------|
| Cocoteros | es |Reading Comprehension| bleu | **3.308** | 2.508 |
| XLSum | es | Summarization | bleu | 1.695 | **1.694** |
### English
#### Classification Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-2B-Instruct | Aitana-2B-S-Instruct |
|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|
| Arc Challenge | en | Question Answering | acc | 0.354 | **0.362** |
| Arc Easy | en | Question Answering | acc | 0.681 | **0.704** |
| Belebele | en | Reading Comprehension | acc | 0.260 | **0.273** |
| PAWS | en | Paraphrasing | acc | 0.597 | **0.610** |
| XNLI | en | Natural Language Inference | acc | 0.512 | **0.553** |
| XStoryCloze | en | Commonsense Reasoning | acc | **0.662** | 0.661 |
| OpenBookQA | en | Question Answering | acc | 0.298 | **0.314** |
| PiQA | en | Question Answering | acc | 0.715 | **0.720** |
| Social iqa | en | Question Answering | acc | **0.453** | 0.431 |
| WNLI | en | Natural Language Inference | acc | **0.535** | 0.451 |
| MGSM Direct | en | Math | exact match | 0.008 | **0.088** |
| TriviaQA | en | Question Answering | exact match | 0.076 | **0.170** |
### 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-2B-S-Instruct against Salamandra-2B-Instruct.
| Task Category | Salamandra-2B-Instruct | Aitana-2B-S-Instruct |
|---------------|------------------------|---------------------------|
| CommonSense reasoning | **2.277 / 1.151** | 2.237 / 1.054 |
| Maths | 1.060 / 0.124 | **1.105 / 0.209** |
| Paraphrasing | **3.518 / 1.308** | 3.517 / 1.151 |
| Reading comprehension | **2.966 / 1.111** | 2.740 / 1.348 |
| Summarization | 2.217 / 1.068 | **2.267 / 0.967** |
| Translation | **3.557 / 0.760** | 3.497 / 0.988 |
| **Overall Avg** | **2.599 / 0.920** | 2.560 / 0.953 |
## 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)**.
### 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 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-SI-Instruct,
author = {Martínez-Murillo, Iván and Sepúlveda-Torres, Robiert and Grande, Eduardo and Galiano, Santiago and Estevanell-Valladares, Ernesto L. 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-2B-SI-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-2B-SI-Instruct}},
note = {Accessed: 2026-05-11}
}
```
---
**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.**