This model is a fine-tuned version of GPT-2, optimized for healthcare-related text generation. It has been trained on healthcare datasets including clinical notes, medical Q&A, medication information, and other medical text data.

The model uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, allowing it to be trained with minimal computational resources while maintaining the base model's general language capabilities.

Uses

Direct Use

This model is intended for:

Generating healthcare-related text
Medical documentation assistance
Clinical note generation
Medical Q&A systems
Healthcare chatbot applications

Example Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "./cpu_healthcare_output")

# Generate text
prompt = "Patient presents with:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

The model was fine-tuned on healthcare datasets including:

Clinical notes
Medical Q&A pairs
Medication information
Lab results and vital signs
Medical history records

Training Hyperparameters

LoRA Rank (r): 8
LoRA Alpha: 16
LoRA Dropout: 0.1
Learning Rate: 5e-5
Batch Size: 1
Max Length: 128-256 tokens

Limitations

This is a small model (GPT-2 base) optimized for CPU training
For better performance, consider using larger models like Mistral-7B with GPU
The model should not be used for actual medical diagnosis or treatment decisions
Always verify medical information with qualified healthcare professionals

Recommendations

Use this model as an assistant tool, not as a replacement for medical professionals
Verify all generated medical information
Consider fine-tuning on domain-specific datasets for specialized use cases
For production use, consider using larger models trained on more extensive datasets

Citation

If you use this model, please cite:

bibtex

@misc{healthcare-gpt2-lora,
  title={Healthcare GPT-2 LoRA Model},
  author={Fine-tuned for healthcare applications},
  year={2024},
  base_model={gpt2}
}

Framework Versions

PEFT 0.17.1
Transformers 4.57.1
PyTorch 2.9.0

debashis2007/healthcare-gpt2

Healthcare GPT-2 LoRA Model

Model Details

Model Description