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🧠 Gemma-2-9B-it — IMB Orthopedics Fine-Tuned Model
This model is a fine-tuned version of unsloth/gemma-2-9b-it, optimized for Italian medical question answering, with a specific focus on orthopedics.
The fine-tuning was performed using a subset of the IMB (Italian Medical Benchmark) dataset, specifically:
- Orthopedics category only
- ~10,000 training samples
The training was performed using the Unsloth library with LoRA fine-tuning, and the adapter weights were later merged into the base model to provide a standalone checkpoint.
This model relies on data from the IMB dataset. If you use this model in research or applications, you must cite the IMB paper (see Citation section below).
📚 Training Dataset — IMB (Italian Medical Benchmark)
IMB is an Italian benchmark for medical question answering, designed to evaluate and improve LLM performance in clinical-domain Italian language understanding and reasoning.
The full dataset includes:
- IMB-QA: 782,644 doctor-patient conversations collected from Italian online medical forums
- IMB-MCQA: 25,862 multiple-choice questions derived from Italian medical specialization exams
⚠️ Important:
This model was trained only on the Orthopedics subset (~10k samples) of IMB, not on the full dataset.
Dataset repository:
👉 https://github.com/PRAISELab-PicusLab/IMB
🧪 Usage Example
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("praiselab-picuslab/gemma-2-9b-it-FT-IMB")
tokenizer = AutoTokenizer.from_pretrained("praiselab-picuslab/gemma-2-9b-it-FT-IMB")
prompt = "Quali sono i sintomi iniziali dell'artrosi del ginocchio?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
⚠️ Usage Restrictions
- Allowed use: Non-commercial research only
- Redistribution: Not allowed without explicit authorization
- Mandatory citation: The IMB dataset paper must be cited in any publication or derived work
Access to this model may be revoked in case of license violation.
📄 Citation
If you use this model, the IMB dataset, or derived outputs in research, please cite:
@inproceedings{DBLP:conf/clic-it/RomanoRBPM25,
author = {Antonio Romano and
Giuseppe Riccio and
Mariano Barone and
Marco Postiglione and
Vincenzo Moscato},
editor = {Cristina Bosco and
Elisabetta Jezek and
Marco Polignano and
Manuela Sanguinetti},
title = {{IMB:} An Italian Medical Benchmark for Question Answering},
booktitle = {Proceedings of the Eleventh Italian Conference on Computational Linguistics
(CLiC-it 2025), Cagliari, Italy, September 24-26, 2025},
series = {{CEUR} Workshop Proceedings},
volume = {4112},
publisher = {CEUR-WS.org},
year = {2025},
url = {https://ceur-ws.org/Vol-4112/92_main_long.pdf}
}
🏗 Training Details
- Base model:
unsloth/gemma-2-9b-it - Fine-tuning method: LoRA (Unsloth)
- Adapter merging: Yes (Full merged model)
- Language: Italian
- Domain: Medical — Orthopedics
- Training size: ~10K samples
📜 License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License.
🤝 Acknowledgements
👨💻 This project was developed by Antonio Romano, Giuseppe Riccio, Mariano Barone, Marco Postiglione, and Vincenzo Moscato at University of Naples, Federico II
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