Update README.md

README.md

@@ -8,20 +8,20 @@ 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, generating output consisting of JSON dictionary corresponding to specified keys.  
+slim-sentiment has been fine-tuned for **sentiment analysis** function calls, generating output consisting of a python dictionary corresponding to specified keys.  
 
 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.  
 
-Inference speed and loading time is much faster with the 'tool' versions of the model.   
+Inference speed and loading time is much faster with the 'tool' versions of the model, and multiple tools can be deployed concurrently and run on a local CPU-based laptop or server.   
 
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
 - **Developed by:** llmware
-- **Model type:** Small, specialized LLM
+- **Model type:** SLIM - small, specialized LLM
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
 - **Finetuned from model:** Tiny Llama 1B
@@ -30,24 +30,49 @@ Inference speed and loading time is much faster with the 'tool' versions of the
 
 <!-- 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.  
+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.  
 
 Example:
 
     text = "The stock market declined yesterday as investors worried increasingly about the slowing economy."   
 
-    model generation - {"sentiment": ["negative"]}
+    model generation output-  {"sentiment": ["negative"]}
 
+    function = "classify"  
     keys = "sentiment"
 
 All of the SLIM models use a novel prompt instruction structured as follows:
 
-    "<human> " + text + "<classify> " + keys + "</classify>" + "/n<bot>: "
+    "<human> " + {text} + "\n" +
+    
+    "<{function}> " + {keys} + "</{function}>" + 
+    
+    "/n<bot>:"
+
+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:
+
+    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.")
 
+    except:
+        print("could not convert to python dictionary automatically - ", output_only)
+        
 ## How to Get Started with the Model
 
-The fastest way to get started with BLING is through direct import in transformers:
+The fastest way to get started with SLIM is through direct import in transformers:
 
     import ast
     from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -74,20 +99,6 @@ The fastest way to get started with BLING is through direct import in transforme
     print("llm_response - ", output_only)
 
     #   where it gets interesting
-    try:
-        # convert llm response output from string to json
-        output_only = ast.literal_eval(output_only)
-        print("converted to json 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.")
-
-    except:
-        print("could not convert to json automatically - ", output_only)
 
    
 ## Using as Function Call in LLMWare