Update README.md

README.md

@@ -2,19 +2,21 @@
 license: apache-2.0
 ---
 
-# SLIM-TOPICS-TOOL
+# SLIM-SA-NER-3B-TOOL
 
 <!-- Provide a quick summary of what the model is/does. -->
 
 
-**slim-topics-tool** is a 4_K_M quantized GGUF version of slim-topics, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.  
+**slim-sa-ner-3b-tool** is a 4_K_M quantized GGUF version of slim-sa-ner-3b, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.  
 
-[**slim-topics**](https://huggingface.co/llmware/slim-topics) is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.
+This model combines two of the most popular traditional classifier capabilities (sentiment analysis and named entity recognition) and re-images them as function calls on a small specialized decoder LLM, generating output in the form of a python dictionary with keys corresponding to sentiment and NER identifiers.  
+
+[**slim-sa-ner-3b**](https://huggingface.co/llmware/slim-sa-ner-3b) is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.
 
 To pull the model via API:  
 
     from huggingface_hub import snapshot_download           
-    snapshot_download("llmware/slim-topics-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)  
+    snapshot_download("llmware/slim-sa-ner-3b-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)  
     
 
 Load in your favorite GGUF inference engine, or try with llmware as follows:
@@ -22,11 +24,11 @@ Load in your favorite GGUF inference engine, or try with llmware as follows:
     from llmware.models import ModelCatalog  
     
     # to load the model and make a basic inference
-    model = ModelCatalog().load_model("slim-topics-tool")
+    model = ModelCatalog().load_model("slim-sa-ner-3b-tool")
     response = model.function_call(text_sample)  
 
     # this one line will download the model and run a series of tests
-    ModelCatalog().tool_test_run("slim-topics-tool", verbose=True)  
+    ModelCatalog().tool_test_run("slim-sa-ner-3b-tool", verbose=True)  
 
 
 Slim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:
@@ -34,11 +36,11 @@ Slim models can also be loaded even more simply as part of a multi-model, multi-
     from llmware.agents import LLMfx
 
     llm_fx = LLMfx()
-    llm_fx.load_tool("topics")
-    response = llm_fx.topics(text)  
+    llm_fx.load_tool("sa-ner")
+    response = llm_fx.sa_ner(text)  
 
 
-Note: please review [**config.json**](https://huggingface.co/llmware/slim-topics-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.  
+Note: please review [**config.json**](https://huggingface.co/llmware/slim-sa-ner-3b-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.  
 
 
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