Update app.py

app.py

@@ -27,15 +27,15 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 peft_model_id = "charansr/llama2-7b-chat-hf-therapist"
 config = PeftConfig.from_pretrained(peft_model_id,
                                    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',
-                                                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',
+#                                                use_auth_token="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")
 
 newtokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path,
                                             use_auth_token="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")
 
 # Load the Lora model
 newmodel = PeftModel.from_pretrained(newmodel, peft_model_id,
-                                    use_auth_token="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")
+                                    use_auth_token="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", load_in_8bit=True, device_map='auto')
 
 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: """