update examples

README.md

@@ -253,32 +253,23 @@ This is a simple example of how to use **Granite-3B-Code-Base** model.
 ```python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-
 device = "cuda" # or "cpu"
 model_path = "ibm-granite/granite-3b-code-base"
-
 tokenizer = AutoTokenizer.from_pretrained(model_path)
-
 # drop device_map if running on CPU
 model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
 model.eval()
-
 # change input text as desired
 input_text = "def generate():"
-
 # tokenize the text
 input_tokens = tokenizer(input_text, return_tensors="pt")
-
 # transfer tokenized inputs to the device
 for i in input_tokens:
     input_tokens[i] = input_tokens[i].to(device)
-
 # generate output tokens
 output = model.generate(**input_tokens)
-
 # decode output tokens into text
 output = tokenizer.batch_decode(output)
-
 # loop over the batch to print, in this example the batch size is 1
 for i in output:
     print(i)