Update app.py

app.py

@@ -23,16 +23,16 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 
 peft_model_id = "charansr/llama2-7b-chat-hf-therapist"
 config = PeftConfig.from_pretrained(peft_model_id,
-                                   token="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")
+                                   use_auth_token="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")
 newmodel = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto',
-                                                token="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")
+                                                use_auth_token="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")
 
 newtokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path,
-                                            token="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")
+                                            use_auth_token="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")
 
 # Load the Lora model
 newmodel = PeftModel.from_pretrained(newmodel, peft_model_id,
-                                    token="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")
+                                    use_auth_token="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")
 
 def givetext(input_text,lmodel,ltokenizer):
   eval_prompt_pt1 = """\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n### Instruction: Act like a therapist and respond\n\n### Input: """