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AI 에이전트 엔지니어링
book

AI 에이전트 엔지니어링

by 마이클 알바다, 강민혁
January 2026
Intermediate
404 pages
7h 22m
Korean
Hanbit Media, Inc.
Content preview from AI 에이전트 엔지니어링
210
2
부
에이전트 시스템의 구축과 확장
출력을 선호해야 하는지 배울 수 있습니다. 데이터셋은
ch07
/
training
_
data
/
dpo
_
it
_
help
_
desk
_
training
_
data
.
jsonl
에서
확인할 수 있습니다.
# 5) 데이터셋 로드
ds = load_dataset("json", data_files=DPO_DATA, split="train")
데이터를 준비했으면 학습 하이퍼파라미터를 정의하고
DPO
설정을 구성합니다.
beta
파라미
터는 최적화 과정에서 모델이 선호되는 응답을 얼마나 강하게 우선시할지를 조절합니다.
# 6) 학습 인자 설정 (사용하지 않음, DPOConfig로 대체)
train_args = TrainingArguments(
output_dir = OUTPUT_DIR,
per_device_train_batch_size = 4,
gradient_accumulation_steps = 4,
learning_rate = 5e-6,
num_train_epochs= 3,
logging_steps = 10,
save_strategy = "epoch",
bf16 = True,
report_to = None,
)
# 7) DPO 학습 설정
dpo_args = DPOConfig(
output_dir = "phi3-mini-helpdesk-dpo", ...
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Publisher Resources

ISBN: 9791175790131