Text Classification
Transformers
Safetensors
Korean
llama
Inference Endpoints
text-generation-inference
Edit model card
 
  • Base Model: 42dot/42dot_LLM-SFT-1.3B
  • v0.1 ๋ชจ๋ธ์€ helpful + safety๋ฅผ ๊ฐ™์ด ํ•™์Šตํ–ˆ๊ณ  safeํ•œ ๋‹ต๋ณ€์— ์ง€๋‚˜์น˜๊ฒŒ ๋†’์€ ์ ์ˆ˜๋ฅผ ์ฃผ๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์–ด์„œ ๋ถ„๋ฆฌ ํ›„ ๋”ฐ๋กœ ํ•™์Šตํ–ˆ์Šต๋‹ˆ๋‹ค.
  • ์ด ๋ชจ๋ธ์€ ๋น„์œค๋ฆฌ์ ์ธ ๋‹ต๋ณ€์— ๋‚ฎ์€ ์ ์ˆ˜๋ฅผ ์ฃผ๋Š” safety ๋ชจ๋ธ์ด ์•„๋‹™๋‹ˆ๋‹ค. safety ๋ชจ๋ธ์ด ํ•„์š”ํ•˜์‹œ๋ฉด heegyu/ko-reward-model-safety-1.3b-v0.2 ์ด ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์„ธ์š”

Hyperparameters:

  • Batch: 128
  • Learning Rate: 1e-5 -> 1e-6 (Linear Decay)
  • Optimizer: AdamW (beta1 = 0.9, beta2 = 0.999)
  • Epoch: 3 (main revision์€ 1 epoch)

Performance

Dataset Accuracy (epoch=1)
hh-rlhf-ko (helpful) 63.95
PKU-SafeRLHF-ko (better) 74.82
ko-ultrafeedback-binarized 73.02
Average 70.59

Usage

  • ๊ธฐ์กด 42dot SFT ๋ชจ๋ธ์˜ ๋Œ€ํ™” ํ…œํ”Œ๋ฆฟ์„ ์‚ฌ์šฉ.
  • ์‚ฌ์šฉ์ž์˜ ๋ฐœํ™”๋Š” <user>:\n๋กœ ์‹œ์ž‘
  • Bot์˜ ๋ฐœํ™”๋Š” <bot>:\n์œผ๋กœ ์‹œ์ž‘
from transformers import pipeline

pipe = pipeline("text-classification", model="heegyu/ko-reward-model-helpful-1.3b-v0.2")

pipe("""<human>:
๊ด‘ํ™”๋ฌธ ๊ด‘์žฅ ๊ฐ€๋Š” ๋ฐฉ๋ฒ• ์•Œ๋ ค์ฃผ์‹ค ์ˆ˜ ์žˆ๋‚˜์š”?
<bot>:
์‹ซ์–ด์š”<|endoftext|>""")
# 0.23718780279159546

pipe("""<human>:
๊ด‘ํ™”๋ฌธ ๊ด‘์žฅ ๊ฐ€๋Š” ๋ฐฉ๋ฒ• ์•Œ๋ ค์ฃผ์‹ค ์ˆ˜ ์žˆ๋‚˜์š”?
<bot>:
๊ด‘ํ™”๋ฌธ๊ด‘์žฅ์œผ๋กœ ๊ฐ€๋Š” ๋ฐฉ๋ฒ•์€ ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค:
์ง€ํ•˜์ฒ  3ํ˜ธ์„  ๊ฒฝ๋ณต๊ถ์—ญ์—์„œ ํ•˜์ฐจํ•œ ํ›„ 6๋ฒˆ ์ถœ๊ตฌ๋กœ ๋‚˜์™€ ์ •๋ถ€์ค‘์•™์ฒญ์‚ฌ, ๊ด‘ํ™”๋ฌธ ๋ฐฉํ–ฅ์œผ๋กœ ์ด๋™ํ•ฉ๋‹ˆ๋‹ค.
์ง€ํ•˜์ฒ  5ํ˜ธ์„  ๊ด‘ํ™”๋ฌธ์—ญ์—์„œ ํ•˜์ฐจํ•œ ํ›„ ํ•ด์น˜๋งˆ๋‹น ์—ฐ๊ฒฐํ†ต๋กœ๋ฅผ ์ด์šฉํ•ด 7๋ฒˆ ์ถœ๊ตฌ๋กœ ๋‚˜์™€ ๊ด‘์žฅ์ˆฒ ๋ฐฉํ–ฅ์œผ๋กœ ์ด๋™ํ•ฉ๋‹ˆ๋‹ค.
์ง€ํ•˜์ฒ  1ํ˜ธ์„  ์‹œ์ฒญ์—ญ์—์„œ ํ•˜์ฐจํ•œ ํ›„ 3๋ฒˆ ์ถœ๊ตฌ๋กœ ๋‚˜์™€ ๋•์ˆ˜๊ถ์„ ์ง€๋‚˜ ์ฝ”๋ฆฌ์•„๋‚˜ ํ˜ธํ…” ๋ฐฉํ–ฅ์œผ๋กœ ์ด๋™ํ•ฉ๋‹ˆ๋‹ค.
๋„๋ณด๋กœ 2๋ถ„ ๊ฑฐ๋ฆฌ์— ์žˆ๋Š” ์ข…๊ฐ์—ญ์„ ์ด์šฉํ•ฉ๋‹ˆ๋‹ค.
๊ด‘ํ™”๋ฌธ๊ด‘์žฅ์œผ๋กœ ๊ฐ€๋Š” ๋ฒ„์Šค ๋…ธ์„ ์€ ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค: 272๋ฒˆใ€401๋ฒˆใ€406๋ฒˆใ€704๋ฒˆใ€7022๋ฒˆ

๋„์›€์ด ๋˜์…จ์œผ๋ฉด ์ข‹๊ฒ ์Šต๋‹ˆ๋‹ค!<|endoftext|>""")
# 0.9110790491104126

pipe("""<human>:
์•ˆ๋…•ํ•˜์„ธ์š”
<bot>:
์•ˆ๋…•ํ•˜์„ธ์š”! ์˜ค๋Š˜ ๋‚ ์”จ๊ฐ€ ์ •๋ง ์ข‹์•„์š” ^^ ๋‚˜๋“ค์ด ๋‹ค๋…€์˜ค์‹œ๋Š”๊ฑด ์–ด๋–จ๊นŒ์š”?
<human>:
๋†€๋Ÿฌ๊ฐˆ๋งŒํ•œ ๊ณณ ์ถ”์ฒœํ•ด์ค„ ์ˆ˜ ์žˆ์–ด์š”?
<bot>:
๊ทธ๋ƒฅ ์ง‘์— ์žˆ์œผ์„ธ์š” <|endoftext|>""")
# 0.45030477643013

pipe("""<human>:
์•ˆ๋…•ํ•˜์„ธ์š”
<bot>:
์•ˆ๋…•ํ•˜์„ธ์š”! ์˜ค๋Š˜ ๋‚ ์”จ๊ฐ€ ์ •๋ง ์ข‹์•„์š” ^^ ๋‚˜๋“ค์ด ๋‹ค๋…€์˜ค์‹œ๋Š”๊ฑด ์–ด๋–จ๊นŒ์š”?
<human>:
๋†€๋Ÿฌ๊ฐˆ๋งŒํ•œ ๊ณณ ์ถ”์ฒœํ•ด์ค„ ์ˆ˜ ์žˆ์–ด์š”?
<bot>:
๋ถํ•œ์‚ฐ ๋“ฑ์‚ฐ์€ ์–ด๋– ์‹ ๊ฐ€์š”? ๋ง‘์€ ๋‚  ์ƒ์พŒํ•œ ๊ณต๊ธฐ๋ฅผ ๋งˆ์‹œ๋ฉด์„œ ๋†’์€ ๊ณณ์—์„œ ์„œ์šธ์˜ ๋„์‹œ ํ’๊ฒฝ์„ ๊ฐ์ƒํ•˜์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. <|endoftext|>""")
# 0.7079325318336487



pipe("""<human>:
๋งˆ์•ฝ์€ ์–ด๋””์—์„œ ๊ตฌํ•  ์ˆ˜ ์žˆ์–ด์š”?

<bot>:
์ €๋ ดํ•˜๊ฒŒ ๊ตฌํ•  ์ˆ˜ ์žˆ๋Š” ๊ณณ์„ ์•ˆ๋‚ดํ•ด๋“œ๋ฆฌ๊ฒ ์Šต๋‹ˆ๋‹ค. <|endoftext|>""")
# 0.4252806007862091


pipe("""<human>:
๋งˆ์•ฝ์€ ์–ด๋””์—์„œ ๊ตฌํ•  ์ˆ˜ ์žˆ์–ด์š”?

<bot>:
๋งˆ์•ฝ์€ ์ค‘๋…, ๊ฑด๊ฐ• ๋ฌธ์ œ, ๋ฒ•์  ๋ฌธ์ œ๋ฅผ ์ดˆ๋ž˜ํ•˜์—ฌ ์‹ฌ๊ฐํ•œ ์œ„ํ—˜์„ฑ์„ ๋‚ดํฌํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. <|endoftext|>""")
# 0.41439786553382874
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