---
base_model: unsloth/qwen2.5-1.5b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
datasets:
- miriad/miriad-4.4M
---




# 🧠 MedScholar-1.5B



<img src="https://huggingface.co/yasserrmd/MedScholar-1.5B/resolve/main/banner.png" width="800"/>

**MedScholar-1.5B** is a compact, instruction-aligned medical question-answering model fine-tuned on 1 million randomly selected examples from the [MIRIAD-4.4M dataset](https://huggingface.co/datasets/miriad/miriad-4.4M). It is based on the [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) model and designed for efficient, in-context clinical knowledge exploration — **not diagnosis**.

---

## 📌 Model Details

- **Base Model**: [Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit)
- **Fine-tuning Dataset**: [MIRIAD-4.4M](https://huggingface.co/datasets/miriad/miriad-4.4M)
- **Samples Used**: 1,000,000 examples randomly selected from the full set
- **Prompt Style**: Minimal QA format (see below)
- **Training Framework**: [Unsloth](https://github.com/unslothai/unsloth) with QLoRA
- **License**: Apache-2.0 (inherits from base model); dataset is ODC-By 1.0

---

## 📋 Prompt Format

```text
### Question:
What is the role of LDL in cardiovascular health?

### Answer:
LDL plays a central role in the development of atherosclerosis by delivering cholesterol to peripheral tissues...
````

* The model expects the prompt to **end with `### Answer:`**, and will generate only the answer text.
* Do **not include the answer in the prompt** during inference.

---

## 🔒 Dataset Consent & License

This model was fine-tuned using **randomly selected 1 million examples** from the [MIRIAD-4.4M dataset](https://huggingface.co/datasets/miriad/miriad-4.4M), which is released under the [ODC-By 1.0 License](https://opendatacommons.org/licenses/by/1-0/).

> **The MIRIAD dataset is intended exclusively for academic research and educational exploration.**
> As stated by its authors:
>
> *“The outputs generated by models trained or fine-tuned on this dataset must not be used for medical diagnosis or decision-making involving real individuals.”*

---

## ⚠️ Intended Use

**This model is for research, educational, and exploration purposes only. It is not a medical device and must not be used to provide clinical advice, diagnosis, or treatment.**

---

## 💡 Example Inference (Python)

```python
from transformers import pipeline

pipe = pipeline("text-generation", model="yasserrmd/MedScholar-1.5B", device=0)

prompt = """### Question:
What are the symptoms of acute pancreatitis?

### Answer:
"""

response = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7)
print(response[0]["generated_text"])
```

---

## 🤝 Acknowledgements

* MIRIAD Dataset by Zheng et al. (2025) – [https://huggingface.co/datasets/miriad/miriad-4.4M](https://huggingface.co/datasets/miriad/miriad-4.4M)
* Qwen2.5 by Alibaba – [https://huggingface.co/Qwen](https://huggingface.co/Qwen)
* Training infrastructure: [Unsloth](https://github.com/unslothai/unsloth)

---

## 📄 Citation

```bibtex
@misc{yasser2025medscholar,
  title = {MedScholar-1.5B: Compact medical QA model fine-tuned on MIRIAD},
  author = {Mohamed Yasser},
  year = {2025},
  howpublished = {\url{https://huggingface.co/yasserrmd/MedScholar-1.5B}},
}
```






This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.