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### model
model_name_or_path: hfl/llama-3-chinese-8b-instruct-v3
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: all
# quantization_bit: 4 # use 4-bit QLoRA
loraplus_lr_ratio: 16.0 # use LoRA+ with lambda=16.0
# use_unsloth: true # use UnslothAI's LoRA optimization for 2x faster training
upcast_layernorm: true
### dataset
dataset: alpaca_mgtv_p1
template: llama3
cutoff_len: 4096
max_samples: 25000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/sft_bf16_p1_full_r3
logging_steps: 10
save_steps: 35
plot_loss: true
# overwrite_output_dir: true
### train
per_device_train_batch_size: 16
gradient_accumulation_steps: 8
learning_rate: 1.0e-4
num_train_epochs: 1.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 35
report_to: wandb
run_name: llama3_8b_p1_full_r3 # optional
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