Update README.md

README.md

@@ -8,9 +8,9 @@ license: apache-2.0
 
 **slim-sentiment-tool** 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-tool is a 4_K_M quantized GGUF version of slim-sentiment, providing a fast, small inference implementation.
+slim-sentiment-tool is a 4_K_M quantized GGUF version of slim-sentiment, providing a small, fast inference implementation.
 
-Load in your favorite GGUF inference engine (see details below on how to set up the prompt template), or try with llmware as follows:
+Load in your favorite GGUF inference engine (see details in config.json to set up the prompt template), or try with llmware as follows:
 
     from llmware.models import ModelCatalog
 
@@ -18,7 +18,7 @@ Load in your favorite GGUF inference engine (see details below on how to set up
     sentiment_tool = ModelCatalog().load_model("slim-sentiment-tool")
     response = sentiment_tool.function_call(text_sample)  
 
-    # this one line will download the model and run a series of tests automatically
+    # this one line will download the model and run a series of tests
     ModelCatalog().test_run("slim-sentiment-tool", verbose=True)  
     
     
@@ -45,18 +45,10 @@ Slim models can also be loaded even more simply as part of a multi-model, multi-
 
 <!-- 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 by combining:  
+SLIM models provide a fast, flexible, intuitive way to integrate classifiers and structured function calls into RAG and LLM application workflows.  
 
-- LLM function calls
-  
-- Agents created with multiple models
-  
-- Small specialized models 'built for purpose'
-  
-- Quantization
+Model instructions, details and test samples have been packaged into the config.json file in the repository, along with the GGUF file.   
 
-Please check out the config.json file included in the repository which includes details on the GGUF model, as well as a set of a 
-test samples. 
 
 Example: