Update app.py

app.py

@@ -54,11 +54,15 @@ 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">
-            TextDiffuser: Diffusion Models as Text Painters
+            ViT Image-to-Text algorithms with LORA
         </h1>   
         <h2 style="text-align: left; font-weight: 450; font-size: 1rem; margin-top: 2rem; margin-bottom: 1.5rem">
-        We propose <b>Image to Text</b>, with ViT model but with LORA fine-tuning. 
+        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.
+        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.        
         </h2>
+        <h3>
+        You can find more info here: [Linkedin article] (https://www.linkedin.com/pulse/fine-tuning-image-to-text-algorithms-withlora-daniel-puente-viejo)
+        </h3>
         </div>
         """)
     gr.inputs.Image(label="Upload any Image", type = 'pil', optional=True)