Update README.md

README.md

@@ -29,14 +29,6 @@ The fine-tuned model understands the nuances about how the Skillate product work
 
 In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens.
 
-E.g. 
-
-system_prompt = "Answer the below query as a customer support assistant about Skillate Product: "
-text = f"<s>[INST] {system_prompt} What are the different ways to log in to the product? [/INST] 
-You can log in to Skillate in any of the three methods here: (https://help.skillate.com/en/support/solutions/articles/82000881022) The conventional method of entering a username and password Using SSO (Single Sign-On) login via Google Using SSO (Single Sign-On) login via Microsoft </s>"
-
-
-
 ## How to Get Started with the Model
 
 from transformers import AutoTokenizer,AutoModelForCausalLM, BitsAndBytesConfig
@@ -56,10 +48,6 @@ base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct
 peft_model = PeftModel.from_pretrained(base_model, "bipulai/mistral-7b-v1-skillate-helpdesk",device_map="auto")
 peft_model.merge_and_unload()
 
-system_prompt = "Answer the below query as a customer support assistant about Skillate Product: "
-question = "How to configure the job approval chain? "
-prompt = f"<s>[INST] {system_prompt} {question} [/INST]"
-
 tokenize = tokenizer(text = [prompt],return_tensors = "pt")
 x = peft_model.generate(input_ids = tokenize["input_ids"].to(device),attention_mask = tokenize["attention_mask"].to(device),max_length = 500)
 response = tokenizer.batch_decode(x,skip_special_tokens=True)