  Chat models are capable of answering a wide range of questions, however, the
accuracy of their responses is highly uncertain. In this research, we propose a
specialized PEFT-MedAware model where we utilize parameter-efficient
fine-tuning (PEFT) to enhance the Falcon-1b large language model on specialized
MedQuAD data consisting of 16,407 medical QA pairs, leveraging only 0.44% of
its trainable parameters to enhance computational efficiency. The paper adopts
data preprocessing and PEFT to optimize model performance, complemented by a
BitsAndBytesConfig for efficient transformer training. The resulting model was
capable of outperforming other LLMs in medical question-answering tasks in
specific domains with greater accuracy utilizing limited computational
resources making it suitable for deployment in resource-constrained
environments. We propose further improvements through expanded datasets, larger
models, and feedback mechanisms for sustained medical relevancy. Our work
highlights the efficiency gains and specialized capabilities of PEFT in medical
AI, outpacing standard models in precision without extensive resource demands.
The proposed model and data are released for research purposes only.

Computation and Language;Artificial Intelligence;Information Retrieval;Machine Learning