Update README.md

README.md

@@ -7,25 +7,28 @@ inference: false
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**slim-sentiment** is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") series, consisting of small, specialized decoder-based models, fine-tuned for function-calling.  
+**slim-sentiment** 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 **sentiment analysis** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:  
 
 `{"sentiment": ["positive"]}`
 
 
-SLIM models re-imagine traditional 'hard-coded' classifiers through the use of function calls, to provide a flexible natural language generative model that can be used as decision gates and processing steps in a complex LLM-based automation workflow.  
+SLIM models aspire re-imagine traditional 'hard-coded' classifiers through the use of function calls, to provide a flexible natural language generative model that can be used as decision gates and processing steps in a complex LLM-based automation workflow.  
 
 Each slim model has a 'quantized tool' version, e.g.,  [**'slim-sentiment-tool'**](https://huggingface.co/llmware/slim-sentiment-tool).  
 
 
 ## Prompt format:
 
-`"<human> " + {text} + "\n" + `
-`"<{function}> " + {keys} + "</{function}>"`
+`"<human> " + {text} + "\n" + `  
+
+`"<{function}> " + {keys} + "</{function}>"`  
+
 `+ "/n<bot>:" `
 
 
+  
 <details>
 <summary><b> Transformers Script </b> </summary>
 
@@ -66,12 +69,10 @@ Each slim model has a 'quantized tool' version, e.g.,  [**'slim-sentiment-tool'*
  
 <details>  
 
-  
-<summary><b>Using as Function Call in LLMWare</b></summary>
 
-We envision the slim models deployed in a pipeline/workflow/templating framework that handles the prompt packaging more elegantly.  
 
-Check out llmware for one such implementation:  
+    
+<summary><b>Using as Function Call in LLMWare</b></summary>
 
     from llmware.models import ModelCatalog
     slim_model = ModelCatalog().load_model("llmware/slim-sentiment")