Update README.md

README.md

@@ -7,12 +7,15 @@ 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, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.  
+**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 has been fine-tuned for **sentiment analysis** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:  
 
-    {"sentiment": ["positive"]}
-            
+`{"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.  
+
 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.  
 
 
@@ -26,11 +29,6 @@ Each slim model has a corresponding 'tool' in a separate repository, e.g.,  [**'
 - **License:** Apache 2.0
 - **Finetuned from model:** Tiny Llama 1B
 
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-The intended use of SLIM models is to 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.  
 
 ## Prompt format:
 
@@ -41,7 +39,7 @@ The intended use of SLIM models is to re-imagine traditional 'hard-coded' classi
     "/n<bot>:"  
     
 <details>
-<summary><b>Getting Started Example: </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")
@@ -79,7 +77,8 @@ The intended use of SLIM models is to re-imagine traditional 'hard-coded' classi
    </details>
 
 
-## Using as Function Call in LLMWare
+<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.  
 
@@ -91,6 +90,8 @@ Check out llmware for one such implementation:
 
     print("llmware - llm_response: ", response)
 
+</details>  
+
     
 ## Model Card Contact