Update README.md

README.md

@@ -49,15 +49,6 @@ The first BLING models have been trained for common RAG scenarios, specifically:
 without the need for a lot of complex instruction verbiage - provide a text passage context, ask questions, and get clear fact-based responses.
 
 
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-1.  BLING is not designed for 'chat-bot' or 'consumer-oriented' applications. 
-
-2.  BLING is not optimal for most production applications, other than simple and highly specific use cases.
-
-
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
@@ -76,7 +67,7 @@ model = AutoModelForCausalLM.from_pretrained("llmware/bling-1b-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:
 
-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: