Update README.md

README.md

@@ -20,7 +20,7 @@ without using any advanced quantization optimizations.
 - **Developed by:** llmware
 - **Model type:** GPTNeoX instruct-trained decoder
 - **Language(s) (NLP):** English
-- **License:** Apache 2.0
+- **License:** CC BY-SA-4.0 [https://creativecommons.org/licenses/by-sa/4.0/]
 - **Finetuned from model:** stabilityai/stablelm-3b-4e1t
 
   
@@ -63,8 +63,8 @@ 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("bling-red-pajamas-3b-0.1")  
-model = AutoModelForCausalLM.from_pretrained("bling-red-pajamas-3b-0.1")  
+tokenizer = AutoTokenizer.from_pretrained("llmware/bling-stable-lm-3b-4e1t-0.1")  
+model = AutoModelForCausalLM.from_pretrained("llmware/bling-stable-lm-3b-4e1t-0.1")  
 
 
 The BLING model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as:
@@ -81,6 +81,16 @@ To get the best results, package "my_prompt" as follows:
 my_prompt = {{text_passage}} + "\n" + {{question/instruction}}
 
 
+## Citations
+
+This model is fine-tuned on the StableLM-3B-4E1T model from StabilityAI.  For more information about this base model, please see the citation below:
+
+@misc{StableLM-3B-4E1T,
+      url={[https://huggingface.co/stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t)},
+      title={StableLM 3B 4E1T},
+      author={Tow, Jonathan and Bellagente, Marco and Mahan, Dakota and Riquelme, Carlos}
+}
+
 
 ## Model Card Contact