Update README.md

README.md

@@ -34,15 +34,18 @@ model = AutoModelForCausalLM.from_pretrained(
 To generate text, use the following Python code:
 
 ```python
-text = """
+context="A New CCn Inverter Circuit For AMLCD Panels Results In Significantly Higher Efficiency And Brightness Mehmet K. Nalbant Linfinity Microelectronics 11861 Western Ave., Garden Grove, CA 92641 Tel: (714) 898-8121 Fax: (714) 893-2570 Abstract - The paper will discuss a new CCFL inverter that is based on a new control technique named the Current Synchronous Zero Voltage Switching (CS-ZVS) topology. This topology enables the design of very eficient dimmable CCFL inverter"
+diaogues= "Speaker 1: What is the name of the new control technique used in the CCFL inverter?\nSpeaker 2: The new control technique is called Current Synchronous Zero Voltage Switching (CS-ZVS) topology.\nSpeaker 1: Can you tell me more about the CS-ZVS topology?\nSpeaker 2: Sure! The CS-ZVS topology is a new control technique that allows for the design of highly efficient and dimmable CCFL inverters.\nSpeaker 1: That sounds impressive. How does it achieve higher efficiency and brightness?\nSpeaker 2: Well, the CS-ZVS topology synchronizes the switching of the inverter with the current flow, resulting in lower losses and improved performance.\nSpeaker 1: I see. So, is this new CCFL inverter already available in the market?\nSpeaker 2: Yes, it is. It was developed by Linfinity Microelectronics and is currently being used in AMLCD panels to improve their efficiency and brightness.\nSpeaker 1: That's amazing. I'm sure this new inverter will be very popular in the market.\nSpeaker 2: ###"
+
+text = f"""
 You will be shown dialogues between Speaker 1 and Speaker 2. Please read and understand given Dialogue Session,
-then complete the task under the guidance of Task Introduction.\n\n
+then complete the task under the guidance of Task Introduction.\n
 Context: {context}
 Dialogue Session: {diaogues}
 Task Introduction: After reading the Dialogue Session, please create an appropriate response in the parts marked ###.
 Task Result:
 """
-inputs = tokenizer(text, return_tensors="pt")
+inputs = tokenizer(text, return_tensors="pt").to('cuda')
 
 outputs = model.generate(**inputs, max_new_tokens=64)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))