Inference sample Code

from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("prismdata/KDI-Llama-3-Open-Ko-8B-Instruct",cache_dir="./", device_map = 'cuda')
tokenizer = AutoTokenizer.from_pretrained("prismdata/KDI-Llama-3-Open-Ko-8B-Instruct",cache_dir="./", device_map = 'cuda')
prompt_template = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\nHuman: {prompt}\nAssistant:\n"
text = 'PMDU(prime ministerโ€™s delivery unit)๊ฐ€ ์–ด๋–ค ์—ญํ• ์„ ํ•˜๋Š” ์กฐ์ง์ธ๊ฐ€์š”?'
model_inputs = tokenizer(prompt_template.format(prompt=text), return_tensors='pt').to("cuda:0")
outputs = model.generate(**model_inputs, max_new_tokens=256).to("cuda:0")
output_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
print(output_text)
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: PMDU(prime ministerโ€™s delivery unit)๊ฐ€ ์–ด๋–ค ์—ญํ• ์„ ํ•˜๋Š” ์กฐ์ง์ธ๊ฐ€์š”?
Assistant:
PMDU๋Š” ์ด๋ฆฌ์‹ค ์‚ฐํ•˜์— ์žˆ๋Š” ์กฐ์ง์œผ๋กœ, ์ •์ฑ…ํšจ๊ณผ์„ฑ ์ฆ๋Œ€๋ฅผ ์œ„ํ•œ ์ง‘ํ–‰๊ณผํ•™์— ๊ด€ํ•œ ์—ฐ๊ตฌ์—์„œ ์ด๋ฆฌ์‹ค์˜ ์ •์ฑ…์กฐ์ •๊ณผ ์ง‘ํ–‰์„ ์ง€์›ํ•˜๋Š” ์—ญํ• ์„ ํ•ฉ๋‹ˆ๋‹ค.
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