Update README.md

README.md

@@ -7,19 +7,13 @@ inference: false
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**slim-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-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.:  
 
-slim-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;`{'sentiment': ['positive'], 'people': ['...'], 'organization': ['...'], 'place': [''] }`
+&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.  
 
-The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs.  
-
-**Compared to encoder classifiers**:  provide intuitive natural language responses that fit more naturally in LLM-based agent processes, generalize better, provide a better fine-tuning base for specialized domains, and offer the potential for combining different classification modalities into a single model architecture.  
-
-**Compared to API-based Mega language models**:  small, specialized, do a few things well, run locally, and do not require complex prompt instructions to generate syntactically correct format and keys.  
+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.  
 
 
 This model is fine-tuned on top of [**llmware/bling-stable-lm-3b-4e1t-v0**](https://huggingface.co/llmware/bling-stable-lm-3b-4e1t-v0), which in turn, is a fine-tune of stabilityai/stablelm-3b-4elt.