Update README.md

README.md

@@ -7,22 +7,22 @@ inference: false
 
 <!-- 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-tags** 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-tags has been fine-tuned for auto-generating relevant tags and points-of-interest function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:  
 
-&nbsp;&nbsp;&nbsp;&nbsp;`{"topics": ["..."]}`
+&nbsp;&nbsp;&nbsp;&nbsp;`{"tags": ["tag1", "tag2", "tag3",...]}`
 
 
 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-tags-tool'**](https://huggingface.co/llmware/slim-tags-tool).  
 
 
 ## Prompt format:
 
 `function = "classify"`  
-`params = "topics"`  
+`params = "tags"`  
 `prompt = "<human> " + {text} + "\n" + `  
 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp;`"<{function}> " + {params} + "</{function}>" + "\n<bot>:"`  
 
@@ -34,10 +34,12 @@ Each slim model has a 'quantized tool' version, e.g.,  [**'slim-topics-tool'**](
     tokenizer = AutoTokenizer.from_pretrained("llmware/slim-topics")
 
     function = "classify"
-    params = "topic"
+    params = "tags"
 
-    text = "The stock market declined yesterday as investors worried increasingly about the slowing economy."  
-    
+    text = "Citibank announced a reduction in its targets for economic growth in France and the UK last week " 
+           "in light of ongoing concerns about inflation and unemployment, especially in large employers " 
+           "such as Airbus."  
+           
     prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:"
 
     inputs = tokenizer(prompt, return_tensors="pt")
@@ -73,8 +75,8 @@ 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-tags")
+    response = slim_model.function_call(text,params=["tags"], function="classify")
 
     print("llmware - llm_response: ", response)