• 📖 Paper: To Adapt or not to Adapt, Rethinking the Value of Medical Knowledge-Aware Large Language Models

How to Get Started with the Model

You can use the model with the transformers pipeline (it uses the Llama 3.1 chat template):

import torch
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="HiTZ/Marmoka-es-it-Llama-3.1-8B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "Eres un experto médico. Responde a la pregunta de opción múltiple únicamente con la letra de la opción correcta."},
    {"role": "user", "content": "¿Qué déficit vitamínico causa el escorbuto?\nA) Vitamina A\nB) Vitamina B12\nC) Vitamina C\nD) Vitamina D"},
]

outputs = pipe(messages, max_new_tokens=256)
print(outputs[0]["generated_text"][-1]["content"])

Model Description

• Developed by: Ane G. Domingo-Aldama, Iker De La Iglesia, Maitane Urruela, Aitziber Atutxa, and Ander Barrena
• Contact: Ane G. Domingo-Aldama and Iker De La Iglesia
• Language(s) (NLP): Spanish
• License: apache-2.0
• Funding:
  • The Spanish Ministry of Science, Innovation and Universities, MCIN/AEI/10.13039/501100011033 projects:
    • EDHIA PID2022-136522OB-C22 (also supported by FEDER, UE).
    • TRUST-MED PID2025-174880OB-I00 (also supported by FEDER, UE).
    • DeepR3 TED2021-130295B-C31 (also supported by the European Union NextGenerationEU/PRTR).
  • Basque Government:
    • Research group funding IT1570-22.
  • HiTZ Center.
  • Predoctoral grants: FPU23/03347 (I. De La Iglesia, Spanish Ministry of Science, Innovation and Universities), PRE_2024_1_0224 (A. G. Domingo-Aldama) and PRE_2025_1_0177 (M. Urruela), Basque Government.

Model Details

Training Data

Limitation and Bias

Biases

Disclaimer

Citing information

@article{domingo2026adapt,
  title={To Adapt or not to Adapt, Rethinking the Value of Medical Knowledge-Aware Large Language Models},
  author={Domingo-Aldama, Ane G and De La Iglesia, Iker and Urruela, Maitane and Atutxa, Aitziber and Barrena, Ander},
  journal={arXiv preprint arXiv:2604.06854},
  year={2026}
}