Hamza_the_doctor.ai

🩺 Hamza_the_doctor.ai — Medical Chatbot (Fine-tuned Gemma 3 270M)

A lightweight medical conversational model fine-tuned on healthcare QA data using Unsloth’s 4-bit optimization, enabling efficient deployment on consumer GPUs while maintaining strong accuracy for medical dialogue.

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🧠 Model Overview

Model Description

• Developed by: Muhammad Hamza Khan
• Base model: unsloth/gemma-3-270m-it-unsloth-bnb-4bit
• Task: Medical Question Answering / Chatbot
• Fine-tuning method: LoRA using PEFT + TRL (SFTTrainer)
• Language(s): English
• Frameworks used: Python, Jupyter Notebook (Kaggle), Unsloth, Transformers, PEFT
• License: Apache 2.0

This model was trained for health-related conversational reasoning — ideal for educational or research use-cases involving patient–doctor-style dialogues.

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🧩 Model Sources

• Repository: HamzaKhan-03/Hamza_the_doctor.ai
• Dataset: lavita/ChatDoctor-HealthCareMagic-100k

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🚀 Usage

from unsloth import FastModel
import torch, os

HF_TOKEN = os.environ["HF_TOKEN"]
HF_REPO_ID = "HamzaKhan-03/Hamza_the_doctor.ai"

# Load model in 4-bit precision
model, tokenizer = FastModel.from_pretrained(
    HF_REPO_ID,
    load_in_4bit=True,
    token=HF_TOKEN,
)
model.to("cuda")

messages = [{"role": "user", "content": "Can you tell me about cancer?"}]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
).removeprefix('<bos>')

inputs = tokenizer(text, return_tensors="pt").to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    top_k=50,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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🧪 Training Details

Dataset

• Name: lavita/ChatDoctor-HealthCareMagic-100k
• Type: Doctor–Patient QA pairs for medical reasoning tasks
• Preprocessing: Filtered for concise Q&A, cleaned for multi-turn context

Hyperparameters

Hardware

• Platform: Kaggle TPU / A100 (16 GB)

• Fine-tuning time: ~30 minutes

• Framework versions:

  • unsloth ≥ 0.7.1
  • peft 0.17.1
  • transformers ≥ 4.44
  • trl ≥ 0.9.6

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📊 Evaluation

⚠️ Note: These metrics are indicative only; not validated for clinical reliability.

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🌍 Environmental Impact (Estimated)

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🧱 Model Architecture

• Base: Gemma 3 270 M (Instruction-Tuned)
• Adapter: LoRA (rank 8)
• Quantization: 4-bit NF4 for reduced memory footprint
• Objective: Supervised Fine-Tuning (SFT) with instruction-style prompts

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🧾 Citation

If you use this model, please cite:

BibTeX:

@misc{hamza2025doctorai,
  title={Hamza_the_doctor.ai — Medical Chatbot (Fine-tuned Gemma 3 270M)},
  author={Muhammad Hamza Khan},
  year={2025},
  howpublished={\url{https://huggingface.co/HamzaKhan-03/Hamza_the_doctor.ai}},
}

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📬 Contact

For technical questions or collaborations: Author: Muhammad Hamza Khan Hugging Face: @HamzaKhan-03

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