Update README.md

README.md

@@ -21,7 +21,7 @@ Inference speed and loading time is much faster with the 'tool' versions of the
 <!-- Provide a longer summary of what this model is. -->
 
 - **Developed by:** llmware
-- **Model type:** SLIM - small, specialized LLM
+- **Model type:** SLIM - small, specialized LLM generating structured outputs
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
 - **Finetuned from model:** Tiny Llama 1B
@@ -53,22 +53,14 @@ For example, in this case, the prompt would be as follows:
 
     "<human>" + "The stock market declined yesterday ..." + "\n" + "<classify> sentiment </classify>" + "\n<bot>:"  
 
-The model generation output will be a string in the form of a well-formed python dictionary, which can be converted as follows:
+The model generation output will be a string in the form of a python dictionary, which can be converted as follows:
 
     try:
-        # convert llm response output from string to json
-        output_only = ast.literal_eval(output_only)
-        print("converted to python dictionary automatically")
-
-        #   look for the key passed in the prompt as a dictionary entry
-        if keys in output_only:
-            if "negative" in output_only[keys]:
-                print("sentiment appears negative - need to handle ...")
-        else:
-            print("response does not appear to include the designated key - will need to try again.")
+        output_only = ast.literal_eval(llm_string_output)
+        print("success - converted to python dictionary automatically")
 
     except:
-        print("could not convert to python dictionary automatically - ", output_only)
+        print("fail - could not convert to python dictionary automatically - ", llm_string_output)
 
    
 ## Using as Function Call in LLMWare
@@ -86,7 +78,8 @@ Check out llmware for one such implementation:
     
 ## Model Card Contact
 
-Darren Oberst & llmware team
+Darren Oberst & llmware team  
 
+[Join us on Discord](https://discord.gg/MhZn5Nc39h)