Update README.md

README.md

@@ -7,10 +7,18 @@ license: cc-by-sa-4.0
 <!-- Provide a quick summary of what the model is/does. -->
 
 
-**slim-sa-ner-tool** is a 4_K_M quantized GGUF version of slim-sa-ner, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.  
+**slim-sa-ner-tool** is a 4_K_M quantized GGUF version of [**slim-sa-ner**](https://huggingface.co/llmware/slim-sa-ner), providing a small, fast inference implementation, optimized for multi-model concurrent deployment.  
+
+slim-sa-ner 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.:
+
+    {'sentiment': ['positive'], people': ['..'], 'organization': ['..'],
+     'place': ['..]}
+
+This 3B parameter '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.
+
+The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs, providing an intuitive, flexible natural language response, without complex prompting, and with improved generalization and ability to fine-tune to a specific domain use case.
 
 
-[**slim-sa-ner**](https://huggingface.co/llmware/slim-sa-ner) 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: