Update README.md

README.md

@@ -3,11 +3,11 @@ license: cc-by-sa-4.0
 inference: false
 ---
 
-# SLIM-SA-NER-3B
+# SLIM-SA-NER
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**slim-sa-ner-3b** combines two of the most popular traditional classifier functions (**Sentiment Analysis** and **Named Entity Recognition**), and reimagines them as function calls on a specialized decoder-based LLM, generating output consisting of a python dictionary with keys corresponding to sentiment, and NER identifiers, such as people, organization, and place, e.g.:  
+**slim-sa-ner** combines two of the most popular traditional classifier functions (**Sentiment Analysis** and **Named Entity Recognition**), and reimagines them as function calls on a specialized decoder-based LLM, generating output consisting of a python dictionary with keys corresponding to sentiment, and NER identifiers, such as people, organization, and place, e.g.:  
 
 &nbsp;&nbsp;&nbsp;&nbsp;`{'sentiment': ['positive'], people': ['..'], 'organization': ['..'],'place': ['..]}`
 
@@ -75,7 +75,7 @@ Each slim model has a 'quantized tool' version, e.g.,  [**'slim-sa-ner-3b-tool'*
 <summary>Using as Function Call in LLMWare</summary>
 
     from llmware.models import ModelCatalog
-    slim_model = ModelCatalog().load_model("llmware/slim-sa-ner-3b")
+    slim_model = ModelCatalog().load_model("llmware/slim-sa-ner")
     response = slim_model.function_call(text,params=["sentiment", "people", "organization", "place"], function="classify")
 
     print("llmware - llm_response: ", response)