Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: apache-2.0
+license: cc-by-sa-4.0
 ---
 
 # Model Card for Model ID
@@ -34,7 +34,7 @@ For test run results (and good indicator of target use cases), please see the fi
 - **Model type:** StableLM-7B
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Finetuned from model:** StableLM-7B
+- **Finetuned from model:** StableLM-Base-Alpha-7B-v2
 
 ## Uses
 
@@ -72,8 +72,8 @@ Any model can provide inaccurate or incomplete information, and should be used i
 The fastest way to get started with BLING is through direct import in transformers:
 
 from transformers import AutoTokenizer, AutoModelForCausalLM  
-tokenizer = AutoTokenizer.from_pretrained("dragon-llama-7b-0.1")  
-model = AutoModelForCausalLM.from_pretrained("dragon-llama-7b-0.1")  
+tokenizer = AutoTokenizer.from_pretrained("dragon-stable-lm-7b-v1")  
+model = AutoModelForCausalLM.from_pretrained("dragon-stable-lm-7b-v1")  
 
 
 The BLING model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as:
@@ -95,7 +95,4 @@ my_prompt = {{text_passage}} + "\n" + {{question/instruction}}
 
 Darren Oberst & llmware team
 
-Please reach out anytime if you are interested in this project!
-
-
-
+Please reach out anytime if you are interested in this project!