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XformAI-india/
Qwen3-1.7B-medicaldataset

Name: Qwen3-1.7B-medicaldataset
Availability: InStock

safetensors

qwen3

medical

FreedomIntelligence/medical-o1-reasoning-SFT

Qwen/Qwen3-1.7B

finetune

mit

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🧠 Qwen3-1.7B-MedicalDataset

A fine-tuned version of Qwen3-1.7B on a curated medical dataset, designed to assist with medical Q&A, clinical documentation, and healthcare-related reasoning. Developed by XformAI-India.

📌 Model Details

Base Model: Qwen3-1.7B (from Qwen by Alibaba)
Architecture: Transformer Decoder (GPT-like)
Parameters: 1.7 Billion
Precision: bfloat16 / float16
Fine-tuning method: Supervised fine-tuning (SFT)
Dataset: FreedomIntelligence/medical-o1-reasoning-SFT

🧪 Intended Use

This model is intended for research and educational purposes in the following domains:

Medical question answering
Summarizing patient records
Medical reasoning and triage
Chatbot integration for healthcare support (non-diagnostic)

🚫 Limitations & Disclaimer

⚠️ Not for clinical use. This model is not a replacement for professional medical advice, diagnosis, or treatment.

May hallucinate or produce incorrect medical information.
Trained only on publicly available or synthetic datasets — not real patient data.
Should not be used in emergency or high-stakes settings.

💡 Example Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-1.7B-medicaldataset", trust_remote_code=True)

prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🏗 Training Configuration

Epochs: 3
Batch Size: 8
Optimizer: AdamW
Learning Rate: 2e-5
Gradient Accumulation: 4
Framework: PyTorch, Hugging Face Transformers

🧠 Citation

If you use this model, please cite:

bibtex

@misc{qwen3medical2025,
  title={Qwen3-1.7B-MedicalDataset: A Fine-Tuned Transformer for Medical AI Research},
  author={XformAI-India},
  year={2025},
  url={https://huggingface.co/XformAI-india/Qwen3-1.7B-medicaldataset}
}