---
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
---

# <span style="color: #7FFF7F;">MedScholar-1.5B GGUF Models</span>


## <span style="color: #7F7FFF;">Model Generation Details</span>

This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`66625a59`](https://github.com/ggerganov/llama.cpp/commit/66625a59a54d0a7504eda4c4e83abfcd83ba1cf8).






---

<a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;">
  Click here to get info on choosing the right GGUF model format
</a>

---



<!--Begin Original Model Card-->





# 🧠 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.


<!--End Original Model Card-->

---

# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>

Help me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**:  

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The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : [Source Code Quantum Network Monitor](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder)

💬 **How to test**:  
 Choose an **AI assistant type**:  
   - `TurboLLM` (GPT-4.1-mini)  
   - `HugLLM` (Hugginface Open-source models)  
   - `TestLLM` (Experimental CPU-only)  

### **What I’m Testing**  
I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:  
- **Function calling** against live network services  
- **How small can a model go** while still handling:  
  - Automated **Nmap security scans**  
  - **Quantum-readiness checks**  
  - **Network Monitoring tasks**  

🟡 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):  
- ✅ **Zero-configuration setup**  
- ⏳ 30s load time (slow inference but **no API costs**) . No token limited as the cost is low.
- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!  

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🟢 **TurboLLM** – Uses **gpt-4.1-mini** :
- **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited. 
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- **Real-time network diagnostics and monitoring**
- **Security Audits**
- **Penetration testing** (Nmap/Metasploit)  

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- 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

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1. `"Give me info on my websites SSL certificate"`  
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4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) to run the .net code on. This is a very flexible and powerful feature. Use with caution!

### Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful.

If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊