Instructions to use SINAI/ALIA-es-legal-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SINAI/ALIA-es-legal-7B-Instruct with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SINAI/ALIA-es-legal-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("SINAI/ALIA-es-legal-7B-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 SINAI/ALIA-es-legal-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SINAI/ALIA-es-legal-7B-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": "SINAI/ALIA-es-legal-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SINAI/ALIA-es-legal-7B-Instruct

SGLang

How to use SINAI/ALIA-es-legal-7B-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 "SINAI/ALIA-es-legal-7B-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": "SINAI/ALIA-es-legal-7B-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 "SINAI/ALIA-es-legal-7B-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": "SINAI/ALIA-es-legal-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

ALIA Spanish Legal and Administrative 7B Instruct Model

Model Description

ALIA Spanish Legal and Administrative 7B Instruct Model is an instruction-tuned language model specialized in the legal and administrative domain for Spanish. This model is derived from SINAI/ALIA-es-legal-7B-Base, which itself is based on the Salamandra-7B model family.

The model has been instruction-tuned using the ALIA-es-legal-synthetic-instructions dataset, enabling it to assist users with legal and administrative queries in Spanish.

DISCLAIMER: This model is provided for research and educational purposes. It should not be used as a substitute for professional legal advice. Users are responsible for ensuring their use of the model complies with applicable laws and regulations. As a result, it may generate harmful or inappropriate content, or legally inaccurate information. Users should verify any legal information generated against official sources. The SINAI Research Group and Barcelona Supercomputing Center shall not be held liable for any outcomes resulting from the use of this model.

Model Lineage

Salamandra-7B (BSC-LT)
    ↓
ALIA-es-legal-7B-Base (SINAI)
    ↓
ALIA-es-legal-7B-Instruct (SINAI) ← This model

Key Features

Specialized Domain: Legal and administrative Spanish language
Instruction-Following: Fine-tuned to respond to user queries and instructions
Foundation: Built upon Salamandra-7B's multilingual capabilities, focused on Spanish
Open License: Released under Apache 2.0 license

Model Details

Architecture

This model maintains the same architecture as its base model ALIA-es-legal-7B-Base, which is derived from Salamandra-7B:


Total Parameters	7,768,117,248
Embedding Parameters	1,048,576,000
Layers	32
Hidden size	4,096
Attention heads	32
Context length	8,192
Vocabulary size	256,000
Precision	bfloat16
Embedding type	RoPE
Activation Function	SwiGLU
Layer normalization	RMS Norm
Flash attention	✅
Grouped Query Attention	✅
Num. query groups	8

Training Details

Instruction Tuning:

Training was conducted by Barcelona Supercomputing Center (BSC)
Dataset: ALIA-es-legal-synthetic-instructions
Language: Spanish
Domain: Legal and administrative
Number of samples: 7,411,809

Training Infrastructure:

Model trained on MareNostrum 5, a pre-exascale EuroHPC supercomputer hosted and operated by Barcelona Supercomputing Center.

The accelerated partition is composed of 1,120 nodes with the following specifications:

4x Nvidia Hopper GPUs with 64GB HBM2 memory
2x Intel Sapphire Rapids 8460Y+ at 2.3GHz and 32c each (64 cores)
4x NDR200 (BW per node 800Gb/s)
512 GB of Main memory (DDR5)
460GB of NVMe storage

The table below specifies the number of nodes and GPUs employed for the supervised fine-tuning:

Phase	Nodes	GPUs	Training Time
SFT	16	64	17h 1m 42s

Training Hyperparameters:

Supervised Fine-Tuning was conducted using an internal fork of the FastChat codebase, adapted to BSC's infrastructure and optimized for stability and efficiency.

Hyperparameter	Value
Learning rate	1e-5
Batch size	1024
Epochs	1
LR Scheduler	Cosine
Warmup Ratio	0.03
NEFTune Noise Alpha	5

Intended Use

Direct Use

This model is designed to assist users with questions and tasks related to legal and administrative matters in Spanish. It can be used for:

Answering legal and administrative queries
Providing information about legal procedures
Assisting with understanding administrative documentation
General legal consultation and guidance

How to Use

Inference

Basic Usage with Transformers

pip install transformers torch accelerate sentencepiece protobuf

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "SINAI/ALIA-es-legal-7B-Instruct"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16
)

# Example legal query
messages = [
    {"role": "user", "content": "¿Cuál es el plazo para presentar un recurso de alzada?"}
]

# Format the input
input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

# Generate response
outputs = model.generate(
    input_ids,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Inference with vLLM

pip install vllm

from vllm import LLM, SamplingParams

model_id = "SINAI/ALIA-es-legal-7B-Instruct"

# Create sampling parameters
sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.95,
    max_tokens=512
)

# Initialize the model
llm = LLM(model=model_id)

# Example prompt
prompt = "¿Cuáles son los requisitos para solicitar una licencia de actividad?"

# Generate response
outputs = llm.generate([prompt], sampling_params)

for output in outputs:
    print(f"Generated text: {output.outputs[0].text}")

Training Data

The model was instruction-tuned using the ALIA-es-legal-synthetic-instructions dataset created by SINAI research group.

This dataset contains synthetic instructions specifically designed for the legal and administrative domain in Spanish, enabling the model to understand and respond to domain-specific queries.

Evaluation

The model was evaluated using the Galtea evaluation platform, which employs LLM-as-judge methodology to assess model performance across various dimensions of quality and accuracy.

Evaluation Methodology

Framework: Galtea Platform

LLM Judges: The evaluation uses multiple state-of-the-art language models as judges to assess response quality:

Gemini 2.5 Flash
GPT-5.2
Claude Sonnet 4.5

Metrics: The model was evaluated using Galtea's custom metrics, specifically:

Answer Relevancy: Measures how relevant and appropriate the model's responses are to the input questions
Factual Accuracy: Assesses the correctness and truthfulness of the information provided in responses

Evaluation Datasets

Justicio RAG QA Dataset

Source: dariolopez/justicio-rag-embedding-qa
Description: A Spanish legal domain question-answering dataset containing 815 instances designed for RAG (Retrieval-Augmented Generation) evaluation
Domain: Legal and administrative Spanish content

Oposiciones Dataset

Description: A collection of questions from Spanish public examination tests (oposiciones)
Domain: Administrative and legal knowledge assessment for public sector positions

Performance Metrics

The following table shows evaluation results comparing ALIA Spanish Legal and Administrative 7B Instruct against the baseline Salamandra 7B Instruct model:

Metric	ALIA Spanish Legal and Administrative 7B Instruct	Salamandra 7B Instruct
Answer Relevancy	93.5	90.6
Factual Accuracy	29.3	19.3
Justicio LLM Judging	0.610273	0.514706
Oposiciones LLM Judging	0.340541	0.268284

Note: The model actually outperforms Salamandra 7B Instruct in all metrics, reflecting the improved precision and correctness concerning legal domain questions.

Overall Performance Summary

Average Input Tokens: 114
Average Output Tokens: 85.9
Overall Average Score: 51%

Limitations and Biases

Known Limitations

Domain Specificity: While specialized in legal and administrative Spanish, the model may not perform optimally on general-purpose tasks
Language: Optimized for Spanish only
Not Legal Advice: Outputs should not be considered as professional legal advice
Training Data Constraints: Performance is limited by the scope and quality of the training data
Potential Hallucinations: Like all language models, may generate plausible-sounding but incorrect information

Bias Considerations

The model inherits potential biases from its base model (Salamandra-7B) and training data
Legal and administrative language may reflect existing biases in legal systems and documentation
Users should be aware of potential biases when using the model for sensitive applications
We recommend additional bias testing and mitigation for specific use cases

Safety and Responsible Use

Human Oversight: Always verify model outputs, especially for critical legal matters
Professional Consultation: Consult with qualified legal professionals for important decisions
Compliance: Ensure use complies with applicable laws and regulations regarding AI systems
Privacy: Do not input sensitive personal or confidential information

Additional Information

License

Apache License, Version 2.0

Citation

@misc{ALIA-es-legal-7B-Instruct,
  title={ALIA Spanish Legal and Administrative 7B Instruct Model},
  author={SINAI Research Group, Universidad de Jaén},
  year={2026},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/datasets/SINAI/ALIA-es-legal-7B-Instruct}}
}

Please also cite the base models:

@misc{ALIA-es-legal-7B-Base,
  title={ALIA Spanish Legal and Administrative 7B Base Model},
  author={SINAI Research Group, Universidad de Jaén},
  year={2026},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/datasets/SINAI/ALIA-es-legal-7B-Base}}
}

@misc{gonzalezagirre2025salamandratechnicalreport,
      title={Salamandra Technical Report}, 
      author={Aitor Gonzalez-Agirre and Marc Pàmies and Joan Llop and Irene Baucells and Severino Da Dalt and Daniel Tamayo and José Javier Saiz and Ferran Espuña and Jaume Prats and Javier Aula-Blasco and Mario Mina and Adrián Rubio and Alexander Shvets and Anna Sallés and Iñaki Lacunza and Iñigo Pikabea and Jorge Palomar and Júlia Falcão and Lucía Tormo and Luis Vasquez-Reina and Montserrat Marimon and Valle Ruíz-Fernández and Marta Villegas},
      year={2025},
      eprint={2502.08489},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.08489}, 
}

Funding

This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project ALIA.

Acknowledgments

Training of this model was conducted thanks to BSC (Barcelona Supercomputing Center) on MareNostrum 5, a pre-exascale EuroHPC supercomputer hosted and operated by them.

Contact: ALIA Project - SINAI Research Group - Universidad de Jaén

More Information: SINAI Research Group | ALIA-UJA Project