gplsi/Aitana-7B-S-Instruct-v0.1
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity
Files
main
Aitana-7B-S-Instruct-v0.1/README.md
ModelHub XC 14f52bfbb8 初始化项目，由ModelHub XC社区提供模型 
Model: gplsi/Aitana-7B-S-Instruct-v0.1
Source: Original Platform
2026-05-20 01:39:35 +08:00

319 lines
20 KiB
Markdown
Raw Permalink Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

---
license: apache-2.0
language:
- ca
- es
- en
base_model: gplsi/Aitana-7B-S-base-1.0
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-7B-S-Instruct-v0.1
**Aitana-7B-S-Instruct-v0.1** 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-7B-S-base-1.0](https://huggingface.co/gplsi/Aitana-7B-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-7B-S-base-1.0](https://huggingface.co/gplsi/Aitana-7B-S-base-1.0) |
| **Architecture** | Transformer decoder-only |
| **Parameters** | ~7.77B |
| **Languages** | Valencian, Spanish, English |
| **License** | Apache 2.0 |
Aitana-7B-S-Instruct-v0.1 is an instruction-tuned variant of Aitana-7B-S-base-1.0, fine-tuned on multilingual instruction data to follow user prompts and generate helpful responses across Valencian, Spanish, and English.
## 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 | — |
The model was NOT instruction-tuned on Catalan data, 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-7B-S-Instruct-v0.1"
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 table, we present the results obtained with different benchmarks from [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) in comparison with [Salamandra-7B-Instruct](https://huggingface.co/BSC-LT/Salamandra-7B-Instruct). The results reflect the instruction-tuned performance of both models.
### Normalized score per language
| Language |Salamandra-7B-Instruct| Aitana-7B-S-Instruct-v0.1 |
|----------|----------|----------|
| Spanish | **0.236** | 0.219 |
| Catalan | **0.343**| 0.304 |
| English | 0.300 | **0.303** |
| Valencian | 0.546 | **0.600** |
### Valencian
#### Classification Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-7B-Instruct | Aitana-7B-S-Instruct-v0.1 |
|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|
| XNLI | va |Natural Language Inference | acc | **0.552** | 0.534 |
#### Generation Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-7B-Instruct | Aitana-7B-S-Instruct-v0.1 |
|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|
| Cocoteros | va |Reading Comprehension | bleu | 6.391 | **8.929** |
| Phrases ca-va | va-ca |Translation - Adaptation | bleu | 67.980 | **81.743** |
| Phrases va-ca | va-ca |Translation - Adaptation | bleu | 79.375 | **83.501** |
| Phrases va-es | va-es |Translation | bleu | 63.104 | **80.329** |
| Phrases es-va | es-va |Translation | bleu | 51.64 | **63.95** |
| Truthfulqa_va | va | Truthfulness | bleu_acc | **0.454** | 0.412 |
### Catalan
#### Classification Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-7B-Instruct | Aitana-7B-S-Instruct-v0.1 |
|------------------------------|--------|---------------------------|-------------|---------------|-----------------------|
| Belebele Cat_latn | ca | Reading Comprehension | acc | **0.718** | 0.581 |
| COPA | ca | Commonsense Reasoning | acc | **0.824** | 0.822 |
| XStoryCloze | ca | Commonsense Reasoning | acc | **0.708** | 0.678 |
| OpenBookQA | ca | Question Answering | acc | **0.374** | 0.36 |
| PAWS | ca | Paraphrasing | acc | **0.671** | 0.662 |
| PiQA | ca | Question Answering | acc | **0.718** | 0.722 |
| ARC Easy | ca | Question Answering | acc | 0.686 | **0.713** |
| ARC Challenge | ca | Question Answering | acc | 0.425 | **0.435** |
| XNLI | ca | Natural Language Inference| acc | **0.559** | 0.540 |
| Teca | ca | Natural Language Inference| acc | **0.557** | 0.522 |
| WNLI | ca | Natural Language Inference| acc | **0.592** | 0.479 |
| Catcola | ca | Linguistic Acceptability | acc | 0.660 | **0.687** |
| Catcola | ca | Linguistic Acceptability | mcc | **0.170** | 0.156 |
| Catalanqa | ca | Question Answering | F1 | **0.576** | 0.526 |
| Mgsm direct | ca | Math | exact match | **0.02** | 0.004 |
| Catalanqa | ca | Question Answering | exact match | **0.259** | 0.176 |
| Xquad | ca | Question Answering | exact match | **0.228** | 0.157 |
| Xquad | ca | Question Answering | F1 | **0.507** | 0.451 |
#### Generation Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-7B-Instruct | Aitana-7B-S-Instruct-v0.1 |
|------------------------------|--------|--------------------------|--------|----------------|-----------------------|
| Cabreu abstractive | ca | Summarization | bleu | 8.60 | **10.10** |
| Cabreu extractive | ca | Summarization | bleu | **39.10** | 28.37 |
| Cabreu extreme | ca | Summarization | bleu | 3.21 | **3.86** |
### Spanish
#### Classification Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-7B-Instruct | Aitana-7B-S-Instruct-v0.1 |
|------------------------------|--------|---------------------------|-------------|---------------|-----------------------|
| Belebele | es | Reading Comprehension | acc | **0.698** | 0.590 |
| PAWS | es | Paraphrasing | acc | **0.629** | 0.626 |
| XNLI | es | Natural Language Inference| acc | **0.487** | 0.485 |
| WNLI | es | Natural Language Inference| acc | **0.549** | 0.493 |
| XStoryCloze | es | Commonsense Reasoning | acc | 0.674 | **0.676** |
| Escola | es | Linguistic Acceptability | acc | 0.577 | **0.681** |
| Escola | es | Linguistic Acceptability | mcc | **0.179** | 0.178 |
| OpenbookQA | es | Question Answering | acc | 0.374 | **0.392** |
| MGSM Direct | es | Math | exact match | **0.100** | **0.100** |
| XQUAD | es | Question Answering | exact match | **0.189** | 0.087 |
| XQUAD | es | Question Answering | F1 | **0.467** | 0.413 |
#### Generation Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-7B-Instruct | Aitana-7B-S-Instruct-v0.1 |
|------------------------------|--------|---------------------|---------|----------------|-----------------------|
| Cocoteros | es |Reading Comprehension| bleu | 6.306 | **8.680** |
| XLSum | es | Summarization | bleu | **2.048** | 1.502 |
### English
#### Classification Benchmarks
| Dataset | Lang. | Task | Metric | Salamandra-7B-Instruct | Aitana-7B-S-Instruct-v0.1 |
|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|
| Arc Challenge | en | Question Answering | acc | 0.478 | **0.523** |
| Arc Easy | en | Question Answering | acc | 0.780 | **0.811** |
| Belebele | en | Reading Comprehension | acc | **0.769** | 0.622 |
| PAWS | en | Paraphrasing | acc | 0.655 | **0.677** |
| XNLI | en | Natural Language Inference | acc | 0.534 | **0.555** |
| XStoryCloze | en | Commonsense Reasoning | acc | **0.729** | 0.716 |
| OpenBookQA | en | Question Answering | acc | **0.348** | 0.340 |
| PiQA | en | Question Answering | acc | 0.781 | **0.784** |
| Social iqa | en | Question Answering | acc | 0.520 | **0.524** |
| WNLI | en | Natural Language Inference | acc | **0.493** | **0.493** |
| MGSM Direct | en | Math | exact match | 0.080 | **0.200** |
| TriviaQA | en | Question Answering | exact match | 0.204 | **0.433** |
### 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-7B-S-Instruct-v0.1 against Salamandra-7B-Instruct.
| Task Category | Salamandra-7B-Instruct | Aitana-7B-S-Instruct-v0.1 |
|---------------|------------------------|---------------------------|
| CommonSense reasoning | 2.637 / 1.295 | **2.989 / 1.200** |
| Maths | 2.386 / 1.536 | **2.584 / 1.474** |
| Paraphrasing | 3.725 / 0.967 | **3.927 / 0.981** |
| Reading comprehension | **3.472 / 1.015** | 3.420 / 1.268 |
| Summarization | **2.369 / 0.932** | 1.862 / 0.713 |
| Translation | 3.770 / 0.580 | **3.895 / 0.814** |
| **Overall Avg** | 3.060 / 1.054 | **3.113 / 1.075** |
## 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-7B-S-base-1.0](https://huggingface.co/gplsi/Aitana-7B-S-base-1.0) - Base version (1.0) of the 7B model
- [gplsi/Aitana-7B-S-Instruct-v0.1](https://huggingface.co/gplsi/Aitana-7B-S-Instruct-v0.1) - Instruction-tuned 7B model
- [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-2B-S-Instruct-v0.1](https://huggingface.co/gplsi/Aitana-2B-S-Instruct-v0.1) - Instruction-tuned 2B model
- [gplsi/Aitana-2B-S-Instruct-Aligned-v0.1](https://huggingface.co/gplsi/Aitana-2B-S-Instruct-Aligned-v0.1) - DPO-aligned instruction-tuned 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://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-7B-S-Instruct-v0.1,
author = {Sepúlveda-Torres, Robiert and Martínez-Murillo, Iván and Grande, Eduardo and Galiano, Santiago 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 7B 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-7B-S-Instruct-v0.1}},
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.**
Reference in New Issue View Git Blame Copy Permalink