# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("WQchoi/QLoRA_test3")
model = AutoModelForCausalLM.from_pretrained("WQchoi/QLoRA_test3", device_map="auto")Quick Links
args=TrainingArguments(
output_dir="C:/Users/tjrja/OneDrive/๋ฐํ ํ๋ฉด/finet/checkpoint4",
load_best_model_at_end=True,
auto_find_batch_size=True,
gradient_accumulation_steps=1,
warmup_ratio=0.1,
num_train_epochs=10,
learning_rate=1e-4,
max_grad_norm=0.5,
weight_decay=0.0001,
fp16=True,
optim="paged_adamw_32bit",
lr_scheduler_type="constant",
logging_steps=264,
evaluation_strategy="epoch",
eval_steps=1,
save_strategy="epoch",
save_steps=10,
)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="WQchoi/QLoRA_test3", device_map="auto")