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

Libraries

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

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

pipe = pipeline("text-generation", model="SINAI/ALIA-es-biomedical-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-biomedical-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("SINAI/ALIA-es-biomedical-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 Settings

vLLM

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

Use Docker

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

SGLang

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

Docker Model Runner
How to use SINAI/ALIA-es-biomedical-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/SINAI/ALIA-es-biomedical-7B-Instruct
```

ALIA Spanish Biomedical 7B Instruct Model

Model Description

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

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

DISCLAIMER: This model is provided for research and educational purposes. It should not be used as a substitute for professional medical advice, diagnosis, or treatment. 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 medically inaccurate information. Users should verify any medical 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-biomedical-7B-Base (SINAI)
    ↓
ALIA-es-biomedical-7B-Instruct (SINAI) ← This model

Key Features

Specialized Domain: Biomedical and clinical 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-biomedical-7B-Base, which is derived from Salamandra-7B:


Base Model	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 SINAI Research Group (Universidad de Jaén)
Dataset: ALIA-es-biomedical-synthetic-instructions
Language: Spanish
Domain: Biomedical and clinical
Number of samples: 328,071

Training Infrastructure:

Model SFT was trained on the CALENDULA supercomputer hosted and operated by SCAYLE (Supercomputación Castilla y León) within the framework of the ALIA project.

The Genoa partition used for SFT training has the following specifications per node:

4x NVIDIA GPUs
AMD EPYC processors
NCCL distributed backend

The table below specifies the node configuration used for the supervised fine-tuning SFT:

Phase	Nodes	GPUs	Training Time
SFT	2	8	~28h 23m

Training Hyperparameters:

Supervised Fine-Tuning was conducted using the Axolotl framework.

Hyperparameter	Value
Learning rate	1.4e-5
Micro batch size	8
Gradient accumulation steps	2
Global batch size	128 (using 8 GPUs)
Epochs	2
LR Scheduler	Cosine
Warmup steps	500
Cosine min. LR ratio	0.10
NEFTune Noise Alpha	5
Sequence length	8,192
Optimizer	adamw_torch_fused
Adam beta1	0.9
Adam beta2	0.94
Adam epsilon	1e-8
Max grad norm	0.28
Weight decay	0.003

Intended Use

Direct Use

This model is designed to assist users with questions and tasks related to the biomedical and clinical domains in Spanish. It can be used for:

Answering biomedical and clinical queries
Summarizing medical literature and clinical documentation
Assisting with understanding clinical guidelines and pharmaceutical information
General medical question answering and search support

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-biomedical-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 medical query
messages = [
    {"role": "user", "content": "¿Cuáles son los síntomas principales de la insuficiencia cardíaca congestiva?"}
]

# 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-biomedical-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 = "¿Qué diferencias hay entre la diabetes tipo 1 y tipo 2?"

# 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-biomedical-synthetic-instructions dataset created by the SINAI research group.

The training dataset has a total of 328,071 instructions, desglosed as follows:

Spanish Biomedical Domain: 144,549
English Biomedical Domain: 50,000
General Spanish: 40,500
General English: 30,000
Original Salamandra Instructions: 63,022

Evaluation

The model was evaluated on several clinical and biomedical benchmarks in Spanish to assess its adaptation to medical query understanding and answering.

Evaluation Datasets

HeadQA (headqa_es)

Source: dvilares/head_qa
Description: A question-answering dataset compiled from official exams for healthcare professionals in Spain (medicine, pharmacy, psychology, etc.).
Domain: Spanish healthcare professional entry exams.

MMLU (Spanish Translation)

Source: cais/mmlu (Spanish translation)
Description: Spanish translations of MMLU medical and biological subjects, evaluated under two modalities: Selection (multiple-choice probabilistically evaluated) and Generative (free-form generation with exact match).
Domain: Professional and academic knowledge in medicine, biology, and chemistry.

Examen de Medicina MIR

Description: A collection of multiple-choice questions corresponding to official entrance exams for medical residency (MIR) in Spain, evaluating clinical reasoning capabilities on complex practical cases and questions with semantic distractors.
Domain: Clinical reasoning and decision-making for medical doctors in Spain.

Performance Metrics

The following tables show the evaluation results comparing ALIA Spanish Biomedical 7B Instruct (Finetuned) against the baseline Salamandra 7B Instruct (Original) model.

HeadQA Results

Evaluation Metric	Salamandra 7B Instruct	ALIA Spanish Biomedical 7B Instruct	Absolute Difference
Raw Accuracy (acc)	36.73%	33.73%	-3.00%
Normalized Accuracy (acc_norm)	41.39%	38.18%	-3.21%

MMLU Clinical and Biological Results (Selection vs. Generative)

MMLU Subject	MMLU Selection (Original)	MMLU Selection (Finetuned)	MMLU Generative (Original)	MMLU Generative (Finetuned)
Anatomy	48.89%	42.96%	43.00%	36.00%
Clinical Knowledge	55.47%	46.79%	48.00%	32.00%
College Biology	50.00%	45.14%	45.00%	29.00%
College Medicine	49.71%	40.46%	41.00%	21.00%
High School Biology	59.03%	44.84%	40.00%	24.00%
Human Aging	62.78%	49.78%	43.00%	33.00%
Medical Genetics	55.00%	44.00%	48.00%	36.00%
Nutrition	54.58%	47.71%	40.00%	37.00%
Professional Medicine	43.75%	47.43%	22.00%	24.00%
Virology	48.80%	45.18%	37.00%	36.00%

MMLU Generative Proxy Results (exact_match)

MMLU Subject (Proxy)	Salamandra 7B Instruct	ALIA Spanish Biomedical 7B Instruct	Absolute Difference
Proxy Biology	39.33%	8.23%	-31.10%
Proxy Health	23.87%	6.84%	-17.03%

Examen de Medicina MIR Results

Task and Metric	Salamandra 7B Instruct	ALIA Spanish Biomedical 7B Instruct	Absolute Difference
MIR MCQ Letter (acc)	38.69%	25.53%	-13.16%
MIR MCQ Option Text (acc_norm)	35.25%	30.78%	-4.47%
MIR MCQ Without Options (acc_norm)	34.37%	30.97%	-3.40%

Limitations and Biases

Known Limitations

Domain Specificity: While specialized in biomedical and clinical Spanish, the model may not perform optimally on general-purpose tasks.
Language: Optimized for Spanish only.
Not Medical Advice: Outputs must not be considered as professional medical advice, diagnosis, or treatment recommendations.
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 or clinically unsafe information.

Bias Considerations

The model inherits potential biases from its base model (Salamandra-7B) and training data.
Medical and clinical language may reflect biases present in scientific literature and medical guidelines.
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 medical matters.
Professional Consultation: Consult with qualified healthcare professionals for medical decisions.
Compliance: Ensure use complies with applicable laws and regulations regarding AI systems.
Privacy: Do not input sensitive personal health or confidential patient information.

Additional Information

License

Apache License, Version 2.0

Citation

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

Please also cite the base models:

@misc{ALIA-es-biomedical-7B-Base,
  title={ALIA Spanish Biomedical 7B Base Model},
  author={SINAI Research Group, Universidad de Jaén},
  year={2026},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/datasets/SINAI/ALIA-es-biomedical-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 SCAYLE (Supercomputación Castilla y León) on the CALENDULA supercomputer, within the framework of the ALIA project.

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

More Information: SINAI Research Group | ALIA-UJA Project