Instructions to use gplsi/Aitana-7B-S-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gplsi/Aitana-7B-S-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="gplsi/Aitana-7B-S-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gplsi/Aitana-7B-S-Instruct")
model = AutoModelForCausalLM.from_pretrained("gplsi/Aitana-7B-S-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use gplsi/Aitana-7B-S-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gplsi/Aitana-7B-S-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gplsi/Aitana-7B-S-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gplsi/Aitana-7B-S-Instruct

SGLang

How to use gplsi/Aitana-7B-S-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "gplsi/Aitana-7B-S-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gplsi/Aitana-7B-S-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "gplsi/Aitana-7B-S-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gplsi/Aitana-7B-S-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gplsi/Aitana-7B-S-Instruct with Docker Model Runner:
```
docker model run hf.co/gplsi/Aitana-7B-S-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Aitana-7B-S-Instruct

Aitana-7B-S-Instruct is an instruction-tuned generative language model from the Aitana family, developed by the GPLSI (Language and Information Systems Group) at the University of Alicante. Built on 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.

Model Description
Evaluation
Training Data
Intended Uses
How to Use
Additional Information

Model Description

Property	Value
Base Model	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 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
ins2	OpenAssistant1 (OASST1)	CA, VA	OpenAssistant/oasst1
ins3	M-Personas	CA, EN, ES, VA	BSC-LT/m-personas
ins4	RAG Multilingual	CA, EN, ES, VA	projecte-aina/RAG_Multilingual
ins5	FLORES	CA, EN, ES	facebook/flores
ins6	Aya Dataset	EN, ES, VA	CohereLabs/aya_dataset
ins7	TowerBlocks	EN, ES	Unbabel/TowerBlocks-v0.2
ins8	Mentor / Mentores	CA, ES, VA	projecte-aina/MentorES / projecte-aina/MentorCA
ins9	Dolly / Dolly 3K	CA, EN, VA	databricks/databricks-dolly-15k
ins10	Alpaca	EN, VA	tatsu-lab/alpaca
ins11	GSM8K	EN, VA	openai/gsm8k
ins12	OpenOrca	EN	Open-Orca/OpenOrca
ins13	No Robots	EN	HuggingFaceH4/no_robots
ins14	CoQCA / CoQCat	CA, VA	projecte-aina/CoQCat
ins15	BOUA	ES	gplsi/boua_parallel
ins16	SciFact	VA	allenai/scifact
ins17	LingComp QA	VA	somosnlp/LingComp_QA
ins18	Instruct Legal Refugiados	VA	somosnlp/instruct-legal-refugiados-es
ins19	Amic-Paralelo	ES	gplsi/amic_parallel

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

import torch
from transformers import pipeline, AutoTokenizer

model_id = "gplsi/Aitana-7B-S-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 table, we present the results obtained with different benchmarks from lm-evaluation-harness in comparison with 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) and the Centro de Inteligencia Digital (CENID), both part of the University of Alicante (UA), 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:

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

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

@misc{gplsi-aitana-7B-S-Instruct,
  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}},
  note         = {Accessed: 2026-05-11}
}

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Tensor type

BF16

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BSC-LT/salamandra-7b

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Collection

This collection features the Aitana family of generative language models developed by the GPLSI research group at Universidad de Alicante. • 22 items • Updated 16 days ago

gplsi
/

Aitana-7B-S-Instruct

Aitana-7B-S-Instruct

Table of Contents

Model Description

Training Data

Intended Uses

How to Use

Transformers

Evaluation

Normalized score per language

Valencian

Classification Benchmarks

Generation Benchmarks

Catalan

Classification Benchmarks

Generation Benchmarks

Spanish

Classification Benchmarks

Generation Benchmarks

English

Classification Benchmarks

Judge Evaluation

Additional Information

Author

Funding

Acknowledgments

License

Disclaimer

Reference

Model tree for gplsi/Aitana-7B-S-Instruct

Datasets used to train gplsi/Aitana-7B-S-Instruct

Collection including gplsi/Aitana-7B-S-Instruct

Aitana