Update README.md

README.md

@@ -8,7 +8,7 @@ license: apache-2.0
 
 **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 has been fine-tuned for **sentiment analysis** function calls, with output of JSON dictionary corresponding to specific named entity keys.  
+slim-sentiment has been fine-tuned for **sentiment analysis** function calls, generating output consisting of JSON dictionary corresponding to specified keys.  
 
 Each slim model has a corresponding 'tool' in a separate repository, e.g., [**'slim-sentiment-tool'**](www.huggingface.co/llmware/slim-sentiment-tool/), which a 4-bit quantized gguf version of the model that is intended to be used for inference.  
 
@@ -47,6 +47,7 @@ All of the SLIM models use a novel prompt instruction structured as follows:
 
 The fastest way to get started with BLING is through direct import in transformers:
 
+    '''python
     import ast
     from transformers import AutoModelForCausalLM, AutoTokenizer
     
@@ -87,10 +88,13 @@ The fastest way to get started with BLING is through direct import in transforme
     except:
         print("could not convert to json automatically - ", output_only)
 
-
+   '''
+   
 ## Using as Function Call in LLMWare
 
-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:  
+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:  
 
     from llmware.models import ModelCatalog
     slim_model = ModelCatalog().load_model("llmware/slim-sentiment")