Update README.md

README.md

@@ -70,7 +70,10 @@ output = tokenizer.decode(output1[0])
 
 print(output)
 
-'''
+```
+### Output
+
+
 Sure, I can help you with that!
 
 Attention mechanisms in transformer models are typically implemented using the attention mechanism in the self-attention layer. Self-attention allows the model to focus on different parts of the input sequence when processing it. This is achieved by computing a set of attention weights, which are used to weigh the contribution of each input element to the output.
@@ -118,9 +121,8 @@ The `query`, `key`, and `value` tensors represent the input sequence to the tran
 The output of the `attention_weights` function is a NumPy tensor that represents the attention weights for the input sequence. These weights are used by the transformer model to weigh the contribution of each input element to the output.
 
 I hope this helps!</s>
-'''
-```
-  
+<hr>
+
 ## Finetuning details
 The finetuning scripts will be available in our [RAIL Github Repository](https://github.com/vmware-labs/research-and-development-artificial-intelligence-lab/tree/main/instruction-tuning)
 ## Evaluation