Update README.md

README.md

@@ -6,7 +6,7 @@ license: apache-2.0
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-bling-sheared-llama-1.3b-0.1 is part of the BLING ("Best Little Instruction-following No-GPU-required") model series, instruct trained on top of a falcon-rw-1b base model.
+bling-sheared-llama-1.3b-0.1 is part of the BLING ("Best Little Instruction-following No-GPU-required") model series, instruct trained on top of a Sheared-LLaMA-1.3B base model.
 
 BLING models are fine-tuned with distilled high-quality custom instruct datasets, targeted at a specific subset of instruct tasks with 
 the objective of providing a high-quality Instruct model that is 'inference-ready' on a CPU laptop even 
@@ -17,7 +17,7 @@ without using any advanced quantization optimizations.
 <!-- Provide a longer summary of what this model is. -->
 
 - **Developed by:** llmware
-- **Model type:** GPTNeoX instruct-trained decoder
+- **Model type:** Instruct-trained decoder 
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
 - **Finetuned from model [optional]:** princeton-nlp/Sheared-LLaMA-1.3B
@@ -53,7 +53,7 @@ without the need for a lot of complex instruction verbiage - provide a text pass
 
 <!-- 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
@@ -67,7 +67,7 @@ model = AutoModelForCausalLM.from_pretrained("llmware/bling-sheared-llama-1.3b-0
 
 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: