🧠 Qwen3-0.6B-MedicalDataset-GGUF

A quantized GGUF-format version of Qwen3-0.6B, fine-tuned on a medical dataset to assist with healthcare-related tasks. Packaged in GGUF format for use with efficient inference engines like llama.cpp. Released by XformAI-India.

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📌 Model Details

• Base Model: Qwen3-0.6B
• Format: GGUF (quantized)
• Quantization Types: Multiple (Q4_K_M, Q5_K_M, Q6_K, Q8_0, etc.)
• Precision: 2-8 bit quantized
• Use Case: Low-resource and edge device inference for medical AI applications

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🧪 Intended Use

This quantized model is intended for:

• Medical Q&A on low-resource devices
• Offline chatbot usage in healthcare education
• Mobile inference for healthcare reasoning

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🚫 Limitations & Disclaimer

⚠️ This model is not intended for clinical use.

• Not suitable for real-time diagnostics or emergency decisions.
• May produce inaccurate or hallucinated medical information.
• Use for research and prototyping only.

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🛠 How to Use

Run with llama.cpp:

./main -m qwen3-0.6b-medical-q4_k_m.gguf -p "Explain symptoms of hypertension."

Or from Python using llama-cpp-python:

from llama_cpp import Llama

llm = Llama(model_path="qwen3-0.6b-medical-q4_k_m.gguf")
output = llm("What are treatment options for Type 2 Diabetes?", max_tokens=200)
print(output)

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🏗 Training Info (Base Fine-Tuning)

• Dataset: FreedomIntelligence/medical-o1-reasoning-SFT
• Epochs: 3
• Batch Size: 8
• Learning Rate: 2e-5
• Framework: PyTorch + Transformers

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🧠 Citation

If you use this model, please cite:

@misc{qwen3medicalgguf2025,
  title={Qwen3-0.6B-MedicalDataset-GGUF: A Quantized Medical AI Model},
  author={XformAI-India},
  year={2025},
  url={https://huggingface.co/XformAI-india/Qwen3-0.6B-medicaldataset-gguf}
}

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