Update README.md

README.md

@@ -7,24 +7,27 @@ inference: false
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**slim-sentiment** is part of the SLIM ("Structured Language Instruction Model") model 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") 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, and to provide a natural language flexible tool that can be used as decision gates and processing steps in a complex LLM-based automation workflow.  
+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.  
 
-Each slim model has a corresponding 'quantized tool' version, e.g.,  [**'slim-sentiment-tool'**](https://huggingface.co/llmware/slim-sentiment-tool).  
+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}>" + "/n<bot>:" ` 
-    
+`"<human> " + {text} + "\n" + `
+`"<{function}> " + {keys} + "</{function}>"`
+`+ "/n<bot>:" `
+
+
 <details>
-<summary><b> Getting Started with Transformers Script </b> </summary>
+<summary><b> Transformers Script </b> </summary>
 
     model = AutoModelForCausalLM.from_pretrained("llmware/slim-sentiment")
     tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sentiment")
@@ -59,12 +62,11 @@ Each slim model has a corresponding 'quantized tool' version, e.g.,  [**'slim-se
     except:
         print("fail - could not convert to python dictionary automatically - ", llm_string_output)
    
-   </details>
-
-
-
+   </details>  
  
-<details>
+<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.