from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-large-seq2seq")
model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-large-seq2seq")
def generate(instruction, knowledge, dialog):
    if knowledge != '':
        knowledge = '[KNOWLEDGE] ' + knowledge
    dialog = ' EOS '.join(dialog)
    query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
    input_ids = tokenizer(f"{query}", return_tensors="pt").input_ids
    outputs = model.generate(input_ids, max_length=128, min_length=8, top_p=0.9, do_sample=True)
    output = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return output
# Instruction for a chitchat task
instruction = f'Instruction: given a dialog context, you need to response empathically.'
# Leave the knowldge empty
knowledge = ''
dialog = [
    'Does money buy happiness?',
    'It is a question. Money buys you a lot of things, but not enough to buy happiness.',
    'What is the best way to buy happiness ?'
]
response = generate(instruction, knowledge, dialog)
print(response)