Update README.md

README.md

@@ -58,7 +58,7 @@ The first BLING models have been trained on question-answering, key-value extrac
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-BLING has not been designed for end consumer-oriented applications, and there has been any focus in training on safeguards to mitigate potential bias.  We would strongly discourage any use of BLING for any 'chatbot' use case.
+BLING has not been designed for end consumer-oriented applications, and there has not been any focus in training on safeguards to mitigate potential bias.  We would strongly discourage any use of BLING for any 'chatbot' use case.
 
 
 ## How to Get Started with the Model
@@ -66,15 +66,13 @@ BLING has not been designed for end consumer-oriented applications, and there ha
 The fastest way to get started with BLING is through direct import in transformers:
 
 from transformers import AutoTokenizer, AutoModelForCausalLM  
-
 tokenizer = AutoTokenizer.from_pretrained("llmware/bling-1.4b-0.1")  
-
 model = AutoModelForCausalLM.from_pretrained("llmware/bling-1.4b-0.1")  
 
 
-The BLING model was fine-tuned with a simple "<human> and <bot> wrapper", so to get the best results, wrap inference entries as:
+The BLING 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: