Update README.md

README.md

@@ -1,28 +1,29 @@
 ---
-license: apache-2.0  
-inference: false  
+license: cc-by-sa-4.0
+inference: false
 ---
 
 # SLIM-TOPICS
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**slim-topics** 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** 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-sentiment has been fine-tuned for **topic analysis** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:  
+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.:  
 
-&nbsp;&nbsp;&nbsp;&nbsp;`{"topics": ["..."]}`
+&nbsp;&nbsp;&nbsp;&nbsp;`{'sentiment': ['positive'], 'people': ['...'], 'organization': ['...'], 'place': [''] }`
 
+This 'combo' model is designed to illustrate the potential power of using function calls on small, specialized models to enable a single model architecture to combine the capabilities of what were traditionally two separate model architectures on an encoder.  
 
 SLIM models are designed to generate structured outputs that can be used programmatically as part of a multi-step, multi-model LLM-based automation workflow.  
 
-Each slim model has a 'quantized tool' version, e.g.,  [**'slim-topics-tool'**](https://huggingface.co/llmware/slim-topics-tool).  
+Each slim model has a 'quantized tool' version, e.g.,  [**'slim-combo-tool-3b'**](https://huggingface.co/llmware/slim-combo-tool-3b).  
 
 
 ## Prompt format:
 
 `function = "classify"`  
-`params = "topics"`  
+`params = "sentiment, person, organization, place"`  
 `prompt = "<human> " + {text} + "\n" + `  
 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp;`"<{function}> " + {params} + "</{function}>" + "\n<bot>:"`  
 
@@ -30,8 +31,8 @@ Each slim model has a 'quantized tool' version, e.g.,  [**'slim-topics-tool'**](
 <details>
 <summary>Transformers Script </summary>
 
-    model = AutoModelForCausalLM.from_pretrained("llmware/slim-topics")
-    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-topics")
+    model = AutoModelForCausalLM.from_pretrained("llmware/slim-combo-sa-ner-3b")
+    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-combo-sa-ner-3b")
 
     function = "classify"
     params = "topic"
@@ -73,8 +74,8 @@ Each slim model has a 'quantized tool' version, e.g.,  [**'slim-topics-tool'**](
 <summary>Using as Function Call in LLMWare</summary>
 
     from llmware.models import ModelCatalog
-    slim_model = ModelCatalog().load_model("llmware/slim-topics")
-    response = slim_model.function_call(text,params=["topics"], function="classify")
+    slim_model = ModelCatalog().load_model("llmware/slim-combo-sa-ner-3b")
+    response = slim_model.function_call(text,params=["sentiment", "people", "organization", "place"], function="classify")
 
     print("llmware - llm_response: ", response)
 
@@ -85,6 +86,4 @@ Each slim model has a 'quantized tool' version, e.g.,  [**'slim-topics-tool'**](
 
 Darren Oberst & llmware team  
 
-[Join us on Discord](https://discord.gg/MhZn5Nc39h)
-
-
+[Join us on Discord](https://discord.gg/MhZn5Nc39h)