Update README.md

README.md

@@ -6,7 +6,7 @@ license: cc-by-sa-4.0
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-dragon-llama-7b-0.1 part of the dRAGon ("Delivering RAG On Private Cloud") model series, RAG-instruct trained on top of a StableLM-7B base model.
+dragon-stablelm-7b-v0 part of the dRAGon ("Delivering RAG On ...") model series, RAG-instruct trained on top of a StableLM-Base-Alpha-7B-v2 base model.
 
 DRAGON models are fine-tuned with high-quality custom instruct datasets, designed for production quality use in RAG scenarios.
 
@@ -33,7 +33,7 @@ For test run results (and good indicator of target use cases), please see the fi
 - **Developed by:** llmware
 - **Model type:** StableLM-7B
 - **Language(s) (NLP):** English
-- **License:** Apache 2.0
+- **License:** CC-BY-SA-4.0
 - **Finetuned from model:** StableLM-Base-Alpha-7B-v2
 
 ## Uses
@@ -69,16 +69,15 @@ 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 BLING is through direct import in transformers:
-
-from transformers import AutoTokenizer, AutoModelForCausalLM  
-tokenizer = AutoTokenizer.from_pretrained("dragon-stable-lm-7b-v1")  
-model = AutoModelForCausalLM.from_pretrained("dragon-stable-lm-7b-v1")  
+The fastest way to get started with dRAGon is through direct import in transformers:
 
+    from transformers import AutoTokenizer, AutoModelForCausalLM  
+    tokenizer = AutoTokenizer.from_pretrained("dragon-stablelm-7b-v0")  
+    model = AutoModelForCausalLM.from_pretrained("dragon-stablelm-7b-v0")  
 
 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:
 
@@ -87,7 +86,7 @@ The BLING model was fine-tuned with closed-context samples, which assume general
 
 To get the best results, package "my_prompt" as follows:
 
-my_prompt = {{text_passage}} + "\n" + {{question/instruction}}
+    my_prompt = {{text_passage}} + "\n" + {{question/instruction}}