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ๅŸบไบŽSakura-14B-Qwen2beta-Base-v2๏ผŒๅœจ815ๆœฌๆ—ฅๆ–‡่ฝปๅฐ่ฏด็š„้Ÿฉ็‰ˆ็ฟป่ฏ‘ๆ•ฐๆฎไธŠๅพฎ่ฐƒ

ๆจกๅž‹ไป…ๆ”ฏๆŒ้Ÿฉๆ–‡โ†’ๆ—ฅๆ–‡็š„็ฟป่ฏ‘

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig

model_path = 'CjangCjengh/LN-ko2ja-14B-v0.1'
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto', trust_remote_code=True).eval()
model.generation_config = GenerationConfig.from_pretrained(model_path, trust_remote_code=True)

# ๆฎต่ฝไน‹้—ด็”จ\nๅˆ†้š”
text = '''์—ฌ์ž์• ๋“ค์ด ์ž์‹ ๋“ค์˜ ์ฒซ ๊ฒฝํ—˜์— ๋Œ€ํ•œ ์ด์•ผ๊ธฐ๋ฅผ ํ•˜๋Š” ๊ฑธ ๋“ค์€ ์ ์ด ์žˆ๋Š”๊ฐ€.
๋ฌผ๋ก  ์—ฌ๊ธฐ์„œ ์ฒซ ๊ฒฝํ—˜์ด๋ผ๋Š” ๊ฒƒ์€ ์ฒ˜์Œ์œผ๋กœ ์•ผ์ž๋ฅผ ์จŒ๋‹ค๋“ ๊ฐ€ ์ฒ˜์Œ์œผ๋กœ ์ˆ ์„ ๋งˆ์…” ๋ดค๋‹ค๋“ ๊ฐ€ ๊ทธ๋Ÿฐ ๊ฒƒ์ด ์•„๋‹ˆ๋ผ, ๋ช…์‹ค๊ณตํžˆ ๊ทธ๋ ‡๊ณ  ๊ทธ๋Ÿฐ ์˜๋ฏธ์—์„œ์˜ ์ฒซ ๊ฒฝํ—˜์ด๋‹ค.
โ€œ์šฐ, ์šฐ๋ฆฌ๊ฐ€โ€ฆโ€ฆ ์ฒ˜์Œ์œผ๋กœ ๊ทธ, ๊ทธ๊ฑธ ํ•œ ๊ฑฐ๋Š” ๋ง์ด์•ผ.โ€
๊ทธ๋ ‡๊ฒŒ ๋งํ•œ ๊ฒƒ์€ ์†ŒํŒŒ์— ์•‰์•„ ์žˆ๋Š” ๊ฐˆ์ƒ‰ ๊ต๋ณต์˜ ์†Œ๋…€์˜€๋‹ค. ๋‘ฅ๊ทผ ์–ผ๊ตด์— ์ปค๋‹ค๋ž€ ๊ฐˆ์ƒ‰ ๋ˆˆ๋™์ž๋ฅผ ์ง€๋‹Œ, ๋ถ€๋“œ๋Ÿฌ์šด ๋จธ๋ฆฌ์นด๋ฝ์„ ์–ด๊นจ ์œ„๋กœ ๋Š˜์–ด๋œจ๋ฆฌ๊ณ  ์žˆ๋Š” ์†Œ๋…€๋‹ค. ์ „๋ฐ˜์ ์œผ๋กœ ์–Œ์ „ํ•œ ๋ชจ๋ฒ”์ƒ ๊ฐ™์•„ ๋ณด์ด๋Š” ์ธ์ƒ์ด๊ณ  ๋ชธ์ง‘๋„ ์•„๋‹ดํ•œ ํŽธ์ด์ง€๋งŒ, ๊ต๋ณต ์ƒ์˜๋ฅผ ๋งคํ˜น์ ์œผ๋กœ ๋ถ€ํ’€์–ด ์˜ค๋ฅด๊ฒŒ ํ•˜๊ณ  ์žˆ๋Š” ๊ฐ€์Šด๋งŒํผ์€ ์–Œ์ „ํ•˜์ง€๋„ ์•„๋‹ดํ•˜์ง€๋„ ์•Š์•˜๋‹ค. ๋ชธ์„ ์›€์ธ ๋ฆฐ ์ž์„ธ ํƒ“์— ๋‘ ํŒ”์ด ๊ฐ€์Šด์„ ์–‘์˜†์—์„œ ์••๋ฐ•ํ•˜๊ณ  ์žˆ์–ด, ๋ชธ์„ ์›€์ง์ผ ๋•Œ๋งˆ๋‹ค ๊ทธ ์œค๊ณฝ์ด ๋ถ€๋“œ๋Ÿฝ๊ฒŒ ์ผ๊ทธ๋Ÿฌ์กŒ๋‹ค.'''

# ๆ–‡ๆœฌ้•ฟๅบฆๆŽงๅˆถๅœจ1024ไปฅๅ†…
assert len(text) < 1024

messages = [
    {'role': 'user', 'content': f'ๆ—ฅๆœฌ่ชžใซ่จณใ—ใฆใใ ใ•ใ„\n{text}'}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors='pt').to('cuda')

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=1024
)

generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
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