๋ชจ๋ธ ์ด๋ฆ
- kogpt2-chatbot-lora
๋ชจ๋ธ ์ค๋ช
- ์ฑ๋ด์ด ์๋กํ๋ค๋ ์ทจ์ง์ ๋ฐ์ดํฐ์ ์ผ๋ก ํ์ธํ๋๋ ํ๊ตญ์ด ์ฑ๋ด ๋ชจ๋ธ
๋ชจ๋ธ ์์ธ
- ๋ฒ ์ด์ค ๋ชจ๋ธ: skt/kogpt2-base-v2
- ํ์ธ ํ๋ ๋ฐฉ๋ฒ: LoRA
- ์ธ์ด: ํ๊ตญ์ด
LoRA ์ค์
r=16,
lora_alpha=32,
target_modules=["c_attn", "c_proj", "c_fc"],
lora_dropout=0.05,
bias="none",
task_type=TaskType.CAUSAL_LM
ํ์ต ์ค์
num_train_epochs=10,
per_device_train_batch_size=4
per_device_eval_batch_size=8,
gradient_accumulation_steps=4,
learning_rate=0.0002,
warmup_steps=100,
logging_steps=50,
eval_strategy= "epoch",
eval_steps=100,
save_strategy= "epoch",
save_steps=100,
load_best_model_at_end=True,
fp16=True,
report_to="none",
weight_decay=0.01,
์ฌ์ฉ ๋ฐฉ๋ฒ
from peft import PeftModel
# ๋ฒ ์ด์ค ๋ชจ๋ธ ๋ก๋ (๋ถ๋ฅ์ฉ)
print("๋ฒ ์ด์ค ๋ชจ๋ธ ๋ก๋ฉ")
base_model_reload = AutoModelForSequenceClassification.from_pretrained(
"klue/bert-base",
num_labels=2
)
# ์
๋ก๋ํ LoRA ์ด๋ํฐ ๋ก๋
print(f"LoRA ์ด๋ํฐ ๋ก๋ฉ: propagation/kogpt2-chatbot-lora")
model_reload = PeftModel.from_pretrained(base_model_reload, model_name_upload)
tokenizer_reload = AutoTokenizer.from_pretrained(model_name_upload)
# GPU๋ก ์ด๋
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_reload = model_reload.to(device)
model_reload.eval()
print("๋ชจ๋ธ ๋ก๋ ์๋ฃ!")
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Base model
skt/kogpt2-base-v2