Update README.md

README.md

@@ -66,21 +66,11 @@ having to send sensitive information over an Internet-based API.
 The first BLING models have been trained for common RAG scenarios, specifically:   question-answering, key-value extraction, and basic summarization as the core instruction types
 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. -->
 
-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.
+Any model can provide inaccurate or incomplete information, and should be used in conjunction with appropriate safeguards and fact-checking mechanisms.
 
 
 ## How to Get Started with the Model
@@ -94,7 +84,7 @@ model = AutoModelForCausalLM.from_pretrained("llmware/bling-falcon-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: