Mungert/MedScholar-1.5B-GGUF

MedScholar-1.5B GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 66625a59.

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🧠 MedScholar-1.5B

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. It is based on the Qwen/Qwen2.5-1.5B-Instruct model and designed for efficient, in-context clinical knowledge exploration — not diagnosis.

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📌 Model Details

• Base Model: Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit
• Fine-tuning Dataset: 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 with QLoRA
• License: Apache-2.0 (inherits from base model); dataset is ODC-By 1.0

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📋 Prompt Format

### 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.

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🔒 Dataset Consent & License

This model was fine-tuned using randomly selected 1 million examples from the MIRIAD-4.4M dataset, which is released under the ODC-By 1.0 License.

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.”

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⚠️ 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.

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💡 Example Inference (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"])

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🤝 Acknowledgements

• MIRIAD Dataset by Zheng et al. (2025) – https://huggingface.co/datasets/miriad/miriad-4.4M
• Qwen2.5 by Alibaba – https://huggingface.co/Qwen
• Training infrastructure: Unsloth

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📄 Citation

@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 and Huggingface's TRL library.

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🚀 If you find these models useful

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

👉 Quantum Network Monitor

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. You will also find the code I use to quantize the models if you want to do it yourself 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!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

• **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
• Create custom cmd processors to run .net code on Quantum Network Monitor Agents
• Real-time network diagnostics and monitoring
• Security Audits
• Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

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

💡 Example commands you could test:

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a comprehensive security audit on my server"
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent 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. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. 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! 😊