Update README.md

README.md

@@ -69,36 +69,6 @@ The model generation output will be a string in the form of a well-formed python
 
     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 SLIM is through direct import in transformers:
-
-    import ast
-    from transformers import AutoModelForCausalLM, AutoTokenizer
-    
-    model = AutoModelForCausalLM.from_pretrained("llmware/slim-sentiment")
-    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sentiment")
-
-    text = "The markets declined for a second straight days on news of disappointing earnings."
-
-    keys = "sentiment"
-
-    prompt = "<human>: " + text + "\n" + "<classify> " + keys + "</classify>" + "\n<bot>: "
-
-    #   huggingface standard generation script
-    inputs = tokenizer(prompt, return_tensors="pt")
-    start_of_output = 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_output:], skip_special_tokens=True)
-
-    print("input text sample - ", text)
-    print("llm_response - ", output_only)
-
-    #   where it gets interesting
 
    
 ## Using as Function Call in LLMWare