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license: mit
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---
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license: mit
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base_model:
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- Qwen/Qwen3-1.7B
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tags:
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- gguf
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- quantized
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- medical
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---
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# 🧠 Qwen3-1.7B-MedicalDataset-GGUF
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A quantized GGUF-format version of [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B), 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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---
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## 📌 Model Details
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- **Base Model**: Qwen3-1.7B
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- **Format**: GGUF (quantized)
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- **Quantization Types**: Multiple
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- **Precision**: 2-8 bit quantized
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- **Use Case**: Low-resource and edge device inference for medical AI applications
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---
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## 🧪 Intended Use
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This quantized model is intended for:
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- Medical Q&A on low-resource devices
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- Offline chatbot usage in healthcare education
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- Mobile inference for healthcare reasoning
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---
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## 🚫 Limitations & Disclaimer
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> ⚠️ **This model is not intended for clinical use.**
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- Not suitable for real-time diagnostics or emergency decisions.
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- May produce inaccurate or hallucinated medical information.
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- Use for research and prototyping only.
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---
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## 🛠 How to Use
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Run with `llama.cpp`:
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```bash
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./main -m qwen3-1.7b-medical-q4_k_m.gguf -p "Explain symptoms of hypertension."
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```
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Or from Python using `llama-cpp-python`:
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```python
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from llama_cpp import Llama
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llm = Llama(model_path="qwen3-1.7b-medical-q4_k_m.gguf")
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output = llm("What are treatment options for Type 2 Diabetes?", max_tokens=200)
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print(output)
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```
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---
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## 🏗 Training Info (Base Fine-Tuning)
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- **Dataset**: FreedomIntelligence/medical-o1-reasoning-SFT
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- **Epochs**: 3
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- **Batch Size**: 8
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- **Learning Rate**: 2e-5
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- **Framework**: PyTorch + Transformers
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---
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## 🧠 Citation
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If you use this model, please cite:
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```bibtex
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@misc{qwen3medicalgguf2025,
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title={Qwen3-1.7B-MedicalDataset-GGUF: A Quantized Medical AI Model},
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author={XformAI-India},
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year={2025},
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url={https://huggingface.co/XformAI-india/Qwen3-1.7B-medicaldataset-gguf}
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}
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```
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---
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