Update README.md

README.md

@@ -6,7 +6,7 @@ license: apache-2.0
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-dragon-red-pajamas-7b-0.1 part of the dRAGon ("Delivering RAG On Private Cloud") model series, RAG-instruct trained on top of a Red-Pajamas-INCITE-7B base model.
+dragon-red-pajama-7b-v0 part of the dRAGon ("Delivering RAG On ...") model series, RAG-instruct trained on top of a RedPajama-INCITE-7B base model.
 
 DRAGON models are fine-tuned with high-quality custom instruct datasets, designed for production quality use in RAG scenarios.
 
@@ -31,10 +31,10 @@ For test run results (and good indicator of target use cases), please see the fi
 <!-- Provide a longer summary of what this model is. -->
 
 - **Developed by:** llmware
-- **Model type:** Red-Pajamas-INCITE-7B
+- **Model type:** RedPajama-INCITE-7B
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Finetuned from model:** Red-Pajamas-INCITE-7B
+- **Finetuned from model:** RedPajama-INCITE-7B
 
 ## Uses
 
@@ -69,16 +69,16 @@ 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:
+The fastest way to get started with dRAGon is through direct import in transformers:
 
-from transformers import AutoTokenizer, AutoModelForCausalLM  
-tokenizer = AutoTokenizer.from_pretrained("dragon-red-pajamas-7b-0.1")  
-model = AutoModelForCausalLM.from_pretrained("dragon-red-pajamas-7b-0.1")  
+    from transformers import AutoTokenizer, AutoModelForCausalLM  
+    tokenizer = AutoTokenizer.from_pretrained("dragon-red-pajama-7b-v0")  
+    model = AutoModelForCausalLM.from_pretrained("dragon-red-pajama-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:
 
@@ -87,15 +87,14 @@ 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}}
 
 
 
 ## Model Card Contact
+Darren Oberst
+llmware
 
-Darren Oberst & llmware team
-
-Please reach out anytime if you are interested in this project!