Instructions to use gplsi/Aitana-2B-S-Instruct-IP-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gplsi/Aitana-2B-S-Instruct-IP-1.0 with Transformers:

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

pipe = pipeline("text-generation", model="gplsi/Aitana-2B-S-Instruct-IP-1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gplsi/Aitana-2B-S-Instruct-IP-1.0")
model = AutoModelForCausalLM.from_pretrained("gplsi/Aitana-2B-S-Instruct-IP-1.0")
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-2B-S-Instruct-IP-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gplsi/Aitana-2B-S-Instruct-IP-1.0"
# 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-2B-S-Instruct-IP-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gplsi/Aitana-2B-S-Instruct-IP-1.0

SGLang

How to use gplsi/Aitana-2B-S-Instruct-IP-1.0 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-2B-S-Instruct-IP-1.0" \
    --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-2B-S-Instruct-IP-1.0",
		"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-2B-S-Instruct-IP-1.0" \
        --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-2B-S-Instruct-IP-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gplsi/Aitana-2B-S-Instruct-IP-1.0 with Docker Model Runner:
```
docker model run hf.co/gplsi/Aitana-2B-S-Instruct-IP-1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Aitana-2B-S-Instruct-IP-1.0

Aitana-2B-S-Instruct-IP-1.0 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 Aitana-2B-S-base-IP-1.0, this model has been fine-tuned to follow instructions across Valencian, Spanish, and English, with a specialized focus on intellectual property domain tasks.

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

Model Description

Property	Value
Base Model	gplsi/Aitana-2B-S-base-IP-1.0
Architecture	Transformer decoder-only
Parameters	~2.25B
Languages	Valencian, Spanish, English
License	Apache 2.0

Aitana-2B-S-Instruct-IP-1.0 is an instruction-tuned variant of Aitana-2B-S-base-IP-1.0, fine-tuned on multilingual instruction data with emphasis on intellectual property applications.

Training Data

This model was instruction fine-tuned using the following data:

Dataset ID	Name	Languages	Source
ins1	InstruCAT	CA	projecte-aina/InstruCAT
ins2	NLUCat	CA	projecte-aina/NLUCat
ins3	Escagleu 64K	CA	projecte-aina/escagleu-64k
ins4	OpenAssistant2 (OASST2)	CA, EN, ES, VA	OpenAssistant/oasst2
ins5	OpenAssistant1 (OASST1)	CA, VA	projecte-aina/oasst1_ca
ins6	M-Personas	CA, EN, ES, VA	BSC-LT/m-personas
ins7	RAG Multilingual	CA, EN, ES	projecte-aina/RAG_Multilingual
ins8	FLORES	CA, EN, ES	facebook/flores
ins9	Aya Dataset	EN, ES, VA	CohereLabs/aya_dataset
ins10	TowerBlocks	EN, ES	Unbabel/TowerBlocks-v0.1
ins11	Mentor / Mentores	CA, ES, VA	projecte-aina/MentorES / projecte-aina/MentorCA
ins12	Dolly / Dolly 3K	CA, EN, VA	databricks/databricks-dolly-15k / projecte-aina/dolly3k_ca
ins13	Alpaca	EN, VA	yahma/alpaca-cleaned
ins14	GSM8K	EN, VA	openai/gsm8k
ins15	OpenOrca	EN	Open-Orca/OpenOrca
ins16	No Robots	EN	HuggingFaceH4/no_robots
ins17	TableGPT	EN	LipengCS/Table-GPT
ins18	CoQCA / CoQCat	CA, VA	projecte-aina/CoQCat
ins19	SciFact	EN, VA	allenai/scifact
ins20	LingComp QA	ES, VA	somosnlp/LingComp_QA
ins21	Instruct Legal Refugiados	ES, VA	somosnlp/instruct-legal-refugiados-es
ins22	Gastronomia Hispana	ES, VA	somosnlp-hackathon-2025/gastronomia-hispana-dpo
ins23	TurismInstructionsGPLSI	VA	—
ins24	Amic-Paralelo	VA	—
ins25	BOUA	VA	gplsi/boua_parallel
ins26	DOGV Parallel	VA	—
ins27	UJI VA-EN Parallel	VA	—
ins28	UJI VA-ES Parallel	VA	—

Intended Uses

This model can be used for:

Instruction following in Valencian, Spanish, and English
Intellectual property domain applications
Chat and conversational applications requiring multilingual support
Text generation with task-specific prompting

How to Use

Transformers

import torch
from transformers import pipeline, AutoTokenizer

model_id = "gplsi/Aitana-2B-S-Instruct-IP-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 = "Explica què és la propietat intel·lectual i quins drets atorga."
result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
print(result[0]['generated_text'])
# Spanish example
text = "Describe los principales tipos de propiedad intelectual y su marco legal."
result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
print(result[0]['generated_text'])
# English example
text = "Explain the concept of intellectual property and its importance in innovation."
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 in comparison with Salamandra-2B-Instruct.

Normalized score per language

Language	Salamandra-2B-Instruct	Aitana-2B-S-Instruct-IP-1.0
Spanish	0.079	0.112
Catalan	0.202	0.182
English	0.178	0.167
Valencian	0.507	0.489
Average	0.242	0.237

Valencian

Classification Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-2B-Instruct	Aitana-2B-S-Instruct-IP-1.0
XNLI	va	Natural Language Inference	acc	0.520	0.501

Generation Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-2B-Instruct	Aitana-2B-S-Instruct-IP-1.0
Cocoteros	va	Reading Comprehension	bleu	2.796	3.204
Phrases ca-va	va-ca	Translation - Adaptation	bleu	58.425	58.694
Phrases va-ca	va-ca	Translation - Adaptation	bleu	70.660	56.706
Phrases va-es	va-es	Translation	bleu	65.427	53.129
Phrases es-va	es-va	Translation	bleu	45.688	43.098
Truthfulqa_va	va	Truthfulness	bleu_acc	0.409	0.381

Catalan

Classification Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-2B-Instruct	Aitana-2B-S-Instruct-IP-1.0
Belebele Cat_latn	ca	Reading Comprehension	acc	0.287	0.253
COPA	ca	Commonsense Reasoning	acc	0.708	0.706
XStoryCloze	ca	Commonsense Reasoning	acc	0.616	0.616
OpenBookQA	ca	Question Answering	acc	0.296	0.270
PAWS	ca	Paraphrasing	acc	0.602	0.603
PiQA	ca	Question Answering	acc	0.638	0.643
SiQA	ca	Question Answering	acc	0.422	0.421
ARC Easy	ca	Question Answering	acc	0.516	0.501
ARC Challenge	ca	Question Answering	acc	0.298	0.299
XNLI	ca	Natural Language Inference	acc	0.513	0.517
Teca	ca	Natural Language Inference	acc	0.486	0.494
WNLI	ca	Natural Language Inference	acc	0.563	0.437
Catcola	ca	Linguistic Acceptability	acc	0.492	0.718
Catcola	ca	Linguistic Acceptability	mcc	0.097	-0.034
Catalanqa	ca	Question Answering	F1	0.516	0.397
Mgsm direct	ca	Math	exact match	0.000	0.000
Catalanqa	ca	Question Answering	exact match	0.182	0.049
Xquad	ca	Question Answering	exact match	0.103	0.055
Xquad	ca	Question Answering	F1	0.394	0.312

Generation Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-2B-Instruct	Aitana-2B-S-Instruct-IP-1.0
Cabreu abstractive	ca	Summarization	bleu	7.610	8.516
Cabreu extractive	ca	Summarization	bleu	38.002	31.230
Cabreu extreme	ca	Summarization	bleu	2.733	3.070

Spanish

Classification Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-2B-Instruct	Aitana-2B-S-Instruct-IP-1.0
Belebele	es	Reading Comprehension	acc	0.268	0.268
PAWS	es	Paraphrasing	acc	0.566	0.623
XNLI	es	Natural Language Inference	acc	0.463	0.442
WNLI	es	Natural Language Inference	acc	0.479	0.451
XStoryCloze	es	Commonsense Reasoning	acc	0.617	0.614
Escola	es	Linguistic Acceptability	acc	0.293	0.662
Escola	es	Linguistic Acceptability	mcc	0.020	0.000
OpenbookQA	es	Question Answering	acc	0.286	0.296
MGSM Direct	es	Math	exact match	0.020	0.060
XQUAD	es	Question Answering	exact match	0.066	0.035
XQUAD	es	Question Answering	F1	0.355	0.292

Generation Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-2B-Instruct	Aitana-2B-S-Instruct-IP-1.0
Cocoteros	es	Reading Comprehension	bleu	3.308	2.755
XLSum	es	Summarization	bleu	1.695	1.474

English

Classification Benchmarks

Dataset	Lang.	Task	Metric	Salamandra-2B-Instruct	Aitana-2B-S-Instruct-IP-1.0
Arc Challenge	en	Question Answering	acc	0.354	0.348
Arc Easy	en	Question Answering	acc	0.681	0.693
Belebele	en	Reading Comprehension	acc	0.260	0.267
PAWS	en	Paraphrasing	acc	0.597	0.602
XNLI	en	Natural Language Inference	acc	0.512	0.547
XStoryCloze	en	Commonsense Reasoning	acc	0.662	0.655
OpenBookQA	en	Question Answering	acc	0.298	0.308
PiQA	en	Question Answering	acc	0.715	0.721
Social iqa	en	Question Answering	acc	0.453	0.419
WNLI	en	Natural Language Inference	acc	0.535	0.437
MGSM Direct	en	Math	exact match	0.008	0.080
TriviaQA	en	Question Answering	exact match	0.076	0.095
CoLA	en	Linguistic Acceptability	mcc	0.055	-0.008

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 against Salamandra-2B-Instruct.

Task Category	Salamandra-2B-Instruct	Aitana-2B-S-Instruct-IP-1.0
CommonSense reasoning	2.277 / 1.151	1.891 / 0.934
Maths	1.060 / 0.124	1.075 / 0.151
Paraphrasing	3.518 / 1.308	3.536 / 1.348
Reading comprehension	2.966 / 1.111	2.599 / 1.331
Summarization	2.217 / 1.068	1.827 / 0.822
Translation	3.557 / 0.760	3.502 / 1.031
Overall Avg	2.599 / 0.920	2.405 / 0.936

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).

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 - Base version (1.0) of the 7B model
gplsi/Aitana-7B-S-Instruct-v0.1 - Instruction-tuned 7B model
gplsi/Aitana-2B-S - Valencian-focused 2B model
gplsi/Aitana-2B-S-base-1.0 - Base version (1.0) of the 2B model
gplsi/Aitana-2B-S-Instruct-v0.1 - Instruction-tuned 2B model
gplsi/Aitana-2B-S-Instruct-Aligned-v0.1 - DPO-aligned instruction-tuned 2B model
gplsi/Aitana-2B-S-Instruct-IP-1.0 - Instruction-tuned 2B model for intellectual property
gplsi/Aitana-2B-S-tourism-Instruct-1.0 - Instruction-tuned 2B model for tourism
gplsi/Aitana-6.3B - Larger 6.3B parameter model
gplsi/Aitana-TA-2B-S - Translation model (2B)
gplsi/Aitana-TA-7B-S - Translation model (7B)
gplsi/Aitana-2B-S-LF - 2B Text Generation variant
gplsi/Aitana-2B-S-tourism-base-1.0 - Domain-specific base model focused on Tourism
gplsi/Aitana-tourism-mb-encoder-1.0 - Tourism domain Fill-Mask/Encoder model
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:

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-2B-S-Instruct-IP-1.0,
  author       = {Martínez-Murillo, Iván and Sepúlveda-Torres, Robiert 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 2B Instruct IP: Instruction-tuned model for intellectual property applications in 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-S-Instruct-IP-1.0}},
  note         = {Accessed: 2026-05-21}
}

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Model size

2B params

Tensor type

BF16

Model tree for gplsi/Aitana-2B-S-Instruct-IP-1.0

Base model

BSC-LT/salamandra-2b

Finetuned

gplsi/Aitana-2B-S-base-IP-1.0

Finetuned

(1)

this model

Quantizations

1 model

gplsi
/

Aitana-2B-S-Instruct-IP-1.0

Aitana-2B-S-Instruct-IP-1.0

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

Part of the Aitana Family

Funding

Acknowledgments

License

Disclaimer

Reference

Model tree for gplsi/Aitana-2B-S-Instruct-IP-1.0

Datasets used to train gplsi/Aitana-2B-S-Instruct-IP-1.0