Update README.md

README.md

@@ -33,14 +33,41 @@ Inference speed and loading time is much faster with the 'tool' versions of the
 
 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:
+<details>
+<summary></summary><b>Getting Started: </b> </summary>
 
-    text = "The stock market declined yesterday as investors worried increasingly about the slowing economy."   
+    model = AutoModelForCausalLM.from_pretrained("llmware/slim-sentiment")
+    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sentiment")
 
-    model generation output-  {"sentiment": ["negative"]}
+    function = "classify"
+    params = "sentiment"
+
+    text = "That was the worst earnings call of the year.  The CEO should be fired."
+    text = "The stock market declined yesterday as investors worried increasingly about the slowing economy."  
+    
+    prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:"
+
+    inputs = tokenizer(prompt, return_tensors="pt")
+    start_of_input = len(inputs.input_ids[0])
+
+    outputs = model.generate(
+        inputs.input_ids.to('cpu'),
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.eos_token_id,
+        do_sample=True,
+        temperature=0.3,
+        max_new_tokens=100
+    )
+
+    output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True)
+
+    print("output only: ", output_only)
+</details>
+
+Sample output:
+
+    {"sentiment": ["negative"]}
 
-    function = "classify"  
-    keys = "sentiment"
 
 All of the SLIM models use a novel prompt instruction structured as follows: