Update README.md

README.md

@@ -7,9 +7,9 @@ inference: false
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**slim-combo-sa-ner-3b** is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling.  
+**slim-sa-ner-3b** is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling.  
 
-slim-combo-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-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.:  
 
 &nbsp;&nbsp;&nbsp;&nbsp;`{'sentiment': ['positive'], 'people': ['...'], 'organization': ['...'], 'place': [''] }`
 
@@ -17,7 +17,7 @@ This 'combo' model is designed to illustrate the potential power of using functi
 
 This model is fine-tuned on top of [**llmware/bling-stable-lm-3b-4e1t-v0**](https://huggingface.co/llmware/bling-stable-lm-3b-4e1t-v0), which in turn, is a fine-tune of stabilityai/stablelm-3b-4elt.
 
-Each slim model has a 'quantized tool' version, e.g.,  [**'slim-combo-tool-3b'**](https://huggingface.co/llmware/slim-combo-tool-3b).  
+Each slim model has a 'quantized tool' version, e.g.,  [**'slim-sa-ner-3b-tool'**](https://huggingface.co/llmware/slim-sa-ner-3b-tool).  
 
 
 ## Prompt format:
@@ -31,8 +31,8 @@ Each slim model has a 'quantized tool' version, e.g.,  [**'slim-combo-tool-3b'**
 <details>
 <summary>Transformers Script </summary>
 
-    model = AutoModelForCausalLM.from_pretrained("llmware/slim-combo-sa-ner-3b")
-    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-combo-sa-ner-3b")
+    model = AutoModelForCausalLM.from_pretrained("llmware/slim-sa-ner-3b")
+    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sa-ner-3b")
 
     function = "classify"
     params = "topic"
@@ -74,7 +74,7 @@ Each slim model has a 'quantized tool' version, e.g.,  [**'slim-combo-tool-3b'**
 <summary>Using as Function Call in LLMWare</summary>
 
     from llmware.models import ModelCatalog
-    slim_model = ModelCatalog().load_model("llmware/slim-combo-sa-ner-3b")
+    slim_model = ModelCatalog().load_model("llmware/slim-sa-ner-3b")
     response = slim_model.function_call(text,params=["sentiment", "people", "organization", "place"], function="classify")
 
     print("llmware - llm_response: ", response)