---
license: cc-by-nc-4.0
language:
- it
tags:
- llama
- llama-3
- meta
- medical-qa
- italian
- biomedical
- question-answering
- fine-tuning
- unsloth
- bnb
- 4bit
- imb
- Ortopedia
datasets:
- praiselab-picuslab/IMB
base_model:
- unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit
---

# 🧠 Llama-3.2-1B-Instruct — IMB Ortopedia Fine-Tuned Model

This model is a fine-tuned version of [`unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit`](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit), optimized for **Italian medical question answering**, with a specific focus on **Ortopedia**.

The fine-tuning was performed using a **subset of the IMB (Italian Medical Benchmark) dataset**, specifically:
- Ortopedia 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 Ortopedia subset (~10,000 samples)** of IMB, not on the full dataset.

Dataset repository:
👉 https://github.com/PRAISELab-PicusLab/IMB

---

## 🧪 Usage Example

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("praiselab-picuslab/Llama-3.2-1B-Instruct-Ortopedia")
tokenizer = AutoTokenizer.from_pretrained("praiselab-picuslab/Llama-3.2-1B-Instruct-Ortopedia")

prompt = "[Example question in Italian about Ortopedia]"
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

---

## 📄 Citation

If you use this model, the IMB dataset, or derived outputs in research, please cite:

```bibtex
@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/Llama-3.2-1B-Instruct-unsloth-bnb-4bit`
* Fine-tuning method: LoRA (Unsloth)
* Quantization: 4-bit (BitsAndBytes)
* Adapter merging: Yes (Full merged model)
* Language: Italian
* Domain: Medical — Ortopedia
* Training size: ~10,000 samples

---

## 📜 License

This work is licensed under a
[Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License][cc-by-nc-nd].

[![CC BY-NC-ND 4.0][cc-by-nc-nd-image]][cc-by-nc-nd]

[cc-by-nc-nd]: http://creativecommons.org/licenses/by-nc-nd/4.0/
[cc-by-nc-nd-image]: https://licensebuttons.net/l/by-nc-nd/4.0/88x31.png
[cc-by-nc-nd-shield]: https://img.shields.io/badge/License-CC%20BY--NC--ND%204.0-lightgrey.svg

---

## 🤝 Acknowledgements

👨‍💻 This project was developed by Mariano Barone, Roberta Di Marino, Francesco Di Serio, Giovanni Dioguardi, Marco Postiglione, Antonio Romano, Giuseppe Riccio, and Vincenzo Moscato at University of Naples, Federico II