Update app.py

app.py

@@ -37,16 +37,13 @@ with gr.Blocks() as demo:
     gr.HTML(
         """
         <div style="text-align: center; max-width: 1200px; margin: 20px auto;">
-        <h1 style="font-weight: 900; font-size: 3rem; margin: 0rem">
+        <h2 style="font-weight: 900; font-size: 3rem; margin: 0rem">
             📸 ViT Image-to-Text with LORA 📝
-        </h1>   
+        </h2>   
         <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.
         <br>
         <br>
-        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.        
-        <br>
-        <br>
         You can find more info here: <u><a href="https://www.linkedin.com/pulse/fine-tuning-image-to-text-algorithms-with-lora-daniel-puente-viejo" target="_blank">Linkedin article</a></u>
         </h2>
         </div>