Update README.md

README.md

@@ -78,14 +78,14 @@ Any model can provide inaccurate or incomplete information, and should be used i
 
 The fastest way to get started with BLING is through direct import in transformers:
 
-from transformers import AutoTokenizer, AutoModelForCausalLM  
-tokenizer = AutoTokenizer.from_pretrained("dragon-yi-6b-0.1")  
-model = AutoModelForCausalLM.from_pretrained("dragon-yi-6b-0.1")  
+    from transformers import AutoTokenizer, AutoModelForCausalLM  
+    tokenizer = AutoTokenizer.from_pretrained("dragon-yi-6b-0.1")  
+    model = AutoModelForCausalLM.from_pretrained("dragon-yi-6b-0.1")  
 
 
 The DRAGON model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as:
 
-full_prompt = "\<human>\: " + my_prompt + "\n" + "\<bot>\:"
+    full_prompt = "\<human>\: " + my_prompt + "\n" + "\<bot>\:"
 
 The BLING model was fine-tuned with closed-context samples, which assume generally that the prompt consists of two sub-parts:
 
@@ -94,7 +94,7 @@ The BLING model was fine-tuned with closed-context samples, which assume general
 
 To get the best results, package "my_prompt" as follows:
 
-my_prompt = {{text_passage}} + "\n" + {{question/instruction}}
+    my_prompt = {{text_passage}} + "\n" + {{question/instruction}}