Update app.py

app.py

@@ -36,11 +36,12 @@ with gr.Blocks() as demo:
         """
         <div style="text-align: center; max-width: 1200px; margin: 20px auto;">
         <h1 style="font-weight: 900; font-size: 3rem; margin: 0rem">
-            ViT Image-to-Text with LORA
+            📸 ViT Image-to-Text with LORA 📝
         </h1>   
         <h2 style="text-align: left; font-weight: 450; font-size: 1rem; margin-top: 2rem; margin-bottom: 1.5rem">
         In the field of large language models, the challenge of fine-tuning has long perplexed researchers. Microsoft, however, has unveiled an innovative solution called <b>Low-Rank Adaptation (LoRA)</b>. With the emergence of behemoth models like GPT-3 boasting billions of parameters, the cost of fine-tuning them for specific tasks or domains has become exorbitant.
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         LoRA offers a groundbreaking approach by freezing the weights of pre-trained models and introducing trainable layers known as <b>rank-decomposition matrices in each transformer block</b>. This ingenious technique significantly reduces the number of trainable parameters and minimizes GPU memory requirements, as gradients no longer need to be computed for the majority of model weights.        
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