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| 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) | |