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Model: yasserrmd/MedScholar-1.5B
Source: Original Platform

README.md

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+---
+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.