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training_arguments = transformers.TrainingArguments(
per_device_train_batch_size=4,
gradient_accumulation_steps=BATCH_SIZE // MICRO_BATCH_SIZE,
warmup_steps=100,
max_steps=300,
learning_rate=3e-4,
fp16=True,
logging_steps=10,
optim="adamw_torch",
evaluation_strategy="steps",
save_strategy="steps",
eval_steps=50,
save_steps=50,
save_total_limit=3,
load_best_model_at_end=True,
)
LORA_R = 8
LORA_ALPHA = 16
LORA_DROPOUT= 0
LORA_TARGET_MODULES = [
"q_proj",
"v_proj",
]
BATCH_SIZE = 128
MICRO_BATCH_SIZE = 4
GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE
LEARNING_RATE = 3e-4
TRAIN_STEPS = 300