Update README.md

README.md

@@ -16,30 +16,15 @@ slim-sentiment has been fine-tuned for **sentiment analysis** function calls, ge
 
 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.  
 
-Each slim model has a corresponding 'tool' in a separate repository, e.g.,  [**'slim-sentiment-tool'**](https://huggingface.co/llmware/slim-sentiment-tool), which a 4-bit quantized gguf version of the model that is intended to be used for inference.  
-
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-- **Developed by:** llmware
-- **Model type:** SLIM - small, specialized LLM generating structured outputs
-- **Language(s) (NLP):** English
-- **License:** Apache 2.0
-- **Finetuned from model:** Tiny Llama 1B
+Each slim model has a corresponding '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> Getting Started with Transformers Script </b> </summary>
 
     model = AutoModelForCausalLM.from_pretrained("llmware/slim-sentiment")
     tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sentiment")
@@ -77,6 +62,8 @@ Each slim model has a corresponding 'tool' in a separate repository, e.g.,  [**'
    </details>
 
 
+
+ 
 <details>
 <summary><b>Using as Function Call in LLMWare</b></summary>