Update the code format according to python syntax.

README.md

@@ -65,49 +65,49 @@ Any model can provide inaccurate or incomplete information, and should be used i
 ## How to Get Started with the Model
 
 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-v0")  
-    model = AutoModelForCausalLM.from_pretrained("dragon-yi-6b-v0")  
-
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM  
+tokenizer = AutoTokenizer.from_pretrained("dragon-yi-6b-v0")  
+model = AutoModelForCausalLM.from_pretrained("dragon-yi-6b-v0")  
+```
 Please refer to the generation_test .py files in the Files repository, which includes 200 samples and script to test the model.  The **generation_test_llmware_script.py** includes built-in llmware capabilities for fact-checking, as well as easy integration with document parsing and actual retrieval to swap out the test set for RAG workflow consisting of business documents.  
 
 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>:"
-
+```python
+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:
 
 1.  Text Passage Context, and
 2.  Specific question or instruction based on the text passage
 
 To get the best results, package "my_prompt" as follows:
-
-    my_prompt = {{text_passage}} + "\n" + {{question/instruction}}
-
+```python
+my_prompt = {{text_passage}} + "\n" + {{question/instruction}}
+```
 
 If you are using a HuggingFace generation script:
-
-    # prepare prompt packaging used in fine-tuning process
-    new_prompt = "<human>: " + entries["context"] + "\n" + entries["query"] + "\n" + "<bot>:"
-
-    inputs = tokenizer(new_prompt, return_tensors="pt")  
-    start_of_output = len(inputs.input_ids[0])
-
-    #   temperature: set at 0.3 for consistency of output
-    #   max_new_tokens:  set at 100 - may prematurely stop a few of the summaries
-
-    outputs = model.generate(
-            inputs.input_ids.to(device),
-            eos_token_id=tokenizer.eos_token_id,
-            pad_token_id=tokenizer.eos_token_id,
-            do_sample=True,
-            temperature=0.3,
-            max_new_tokens=100,
-            )
-
-    output_only = tokenizer.decode(outputs[0][start_of_output:],skip_special_tokens=True)  
-
+```python
+# prepare prompt packaging used in fine-tuning process
+new_prompt = "<human>: " + entries["context"] + "\n" + entries["query"] + "\n" + "<bot>:"
+
+inputs = tokenizer(new_prompt, return_tensors="pt")  
+start_of_output = len(inputs.input_ids[0])
+
+#   temperature: set at 0.3 for consistency of output
+#   max_new_tokens:  set at 100 - may prematurely stop a few of the summaries
+
+outputs = model.generate(
+        inputs.input_ids.to(device),
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.eos_token_id,
+        do_sample=True,
+        temperature=0.3,
+        max_new_tokens=100,
+        )
+
+output_only = tokenizer.decode(outputs[0][start_of_output:],skip_special_tokens=True)  
+```
 
 ## Model Card Contact