Update README.md

README.md

@@ -7,11 +7,11 @@ inference: false
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**slim-summary** is a small, specialized model finetuned for summarize function-calls, generating output consisting of a python dictionary with a "summary" key, and a value that consists of a list of distinct summary points.  
+**slim-summary** is a small, specialized model finetuned for summarize function-calls, generating output consisting of a python list of distinct summary points.  
 
 As an experimental feature in the model, there is an optional list size that can be passed with the parameters in invoking the model to guide the model to a specific number of response elements.  
 
-&nbsp;&nbsp;&nbsp;&nbsp;`{'summary': ['point1', 'point2', 'point3']}`
+&nbsp;&nbsp;&nbsp;&nbsp;`['summary_point1', 'summary_point2', 'summary_point3']`
 
 This model is 2.7B parameters, small enough to run on a CPU, and is fine-tuned on top of [**llmware/bling-stable-lm-3b-4e1t-v0**](https://huggingface.co/llmware/bling-stable-lm-3b-4e1t-v0), which in turn, is a fine-tune of stabilityai/stablelm-3b-4elt.
 
@@ -68,8 +68,11 @@ For fast inference use of this model, we would recommend using the 'quantized to
         output_only = ast.literal_eval(llm_string_output)
         print("success - converted to python dictionary automatically")
     except:
+        # note: rules-based conversion may be required - see [llmware-models.py](www.github.com/llmware-ai/llmware/blobs/main/llmware/models.py) ModelCatalog.remediate_function_call_string()
+        # for good example of post-processing conversion script
+        
         print("fail - could not convert to python dictionary automatically - ", llm_string_output)
-   
+     
    </details>  
  
 <details>