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README.md

@@ -26,15 +26,16 @@ python nanogpt_slm_instruct_inference.py
 # Import loads the model automatically (one-time download from HuggingFace)
 from nanogpt_slm_instruct_inference import ask
 
+## First time  execution will O/P prefed 5 examples with model responses
 # Simple question
 print(ask("What is the capital of France?"))
-
+print()
 # With input context
 print(ask(
     instruction="Summarize the following text.",
     input_text="Machine learning enables systems to learn from data rather than being explicitly programmed."
 ))
-
+print()
 # Control generation
 print(ask(
     "Write a short poem about the ocean.",
@@ -42,6 +43,7 @@ print(ask(
     top_k=100,          # wider sampling pool
     max_tokens=150      # longer output
 ))
+print()
 ```
 
 ### Option 3: Load weights manually
@@ -49,7 +51,10 @@ print(ask(
 from huggingface_hub import hf_hub_download
 import torch, tiktoken
 
-model_path = hf_hub_download(repo_id="nishantup/nanogpt-slm-instruct", filename="nanogpt_slm_instruct.pth")
+repo_id= "nishantup/nanogpt-slm-instruct"
+filename = "nanogpt_slm_instruct.pth"
+
+model_path = hf_hub_download(repo_id=repo_id, filename=filename)
 
 # Build model (full architecture in nanogpt_slm_instruct_inference.py)
 from nanogpt_slm_instruct_inference import GPT, GPTConfig, generate, format_input