  $ $The synergy of language and vision models has given rise to Large Language
and Vision Assistant models (LLVAs), designed to engage users in rich
conversational experiences intertwined with image-based queries. These
comprehensive multimodal models seamlessly integrate vision encoders with Large
Language Models (LLMs), expanding their applications in general-purpose
language and visual comprehension. The advent of Large Multimodal Models (LMMs)
heralds a new era in Artificial Intelligence (AI) assistance, extending the
horizons of AI utilization. This paper takes a unique perspective on LMMs,
exploring their efficacy in performing image classification tasks using
tailored prompts designed for specific datasets. We also investigate the LLVAs
zero-shot learning capabilities. Our study includes a benchmarking analysis
across four diverse datasets: MNIST, Cats Vs. Dogs, Hymnoptera (Ants Vs. Bees),
and an unconventional dataset comprising Pox Vs. Non-Pox skin images. The
results of our experiments demonstrate the model's remarkable performance,
achieving classification accuracies of 85\%, 100\%, 77\%, and 79\% for the
respective datasets without any fine-tuning. To bolster our analysis, we assess
the model's performance post fine-tuning for specific tasks. In one instance,
fine-tuning is conducted over a dataset comprising images of faces of children
with and without autism. Prior to fine-tuning, the model demonstrated a test
accuracy of 55\%, which significantly improved to 83\% post fine-tuning. These
results, coupled with our prior findings, underscore the transformative
potential of LLVAs and their versatile applications in real-world scenarios.
