Update README.md

README.md

@@ -3,7 +3,7 @@ license: apache-2.0
 inference: false  
 ---
 
-# Model Card for Model ID
+# SLIM-TOPICS
 
 <!-- Provide a quick summary of what the model is/does. -->
 
@@ -11,10 +11,10 @@ inference: false
 
 slim-sentiment has been fine-tuned for **topic analysis** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:  
 
-&nbsp;&nbsp;&nbsp;&nbsp;`{"topic": ["..."]}`
+&nbsp;&nbsp;&nbsp;&nbsp;`{"topics": ["..."]}`
 
 
-SLIM models are designed to provide a flexible natural language generative model that can be used as part of a multi-step, multi-model LLM-based automation workflow.  
+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).  
 
@@ -22,7 +22,7 @@ Each slim model has a 'quantized tool' version, e.g.,  [**'slim-topics-tool'**](
 ## Prompt format:
 
 `function = "classify"`  
-`params = "topic"`  
+`params = "topics"`  
 `prompt = "<human> " + {text} + "\n" + `  
 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp;`"<{function}> " + {params} + "</{function}>" + "\n<bot>:"`  
 
@@ -74,7 +74,7 @@ Each slim model has a 'quantized tool' version, e.g.,  [**'slim-topics-tool'**](
 
     from llmware.models import ModelCatalog
     slim_model = ModelCatalog().load_model("llmware/slim-topics")
-    response = slim_model.function_call(text,params=["topic"], function="classify")
+    response = slim_model.function_call(text,params=["topics"], function="classify")
 
     print("llmware - llm_response: ", response)