Update README.md

README.md

@@ -36,9 +36,6 @@ For test run results (and good indicator of target use cases), please see the fi
 - **License:** Apache 2.0
 - **Finetuned from model:** TinyLlama-1.1b - 2.5T checkpoint
 
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 ### Direct Use
 
@@ -60,7 +57,7 @@ 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 dRAGon is through direct import in transformers:
+The fastest way to get started with BLING is through direct import in transformers:
 
     from transformers import AutoTokenizer, AutoModelForCausalLM  
     tokenizer = AutoTokenizer.from_pretrained("bling-tiny-llama-v0", trust_remote_code=True)  
@@ -68,7 +65,7 @@ The fastest way to get started with dRAGon is through direct import in transform
 
 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:
+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>:"