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### model | |
model_name_or_path: shenzhi-wang/Llama3-8B-Chinese-Chat | |
#model_name_or_path: FlagAlpha/Llama3-Chinese-8B-Instruct | |
### 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_p2 | |
template: llama3 | |
cutoff_len: 8192 | |
max_samples: 25000 | |
overwrite_cache: true | |
preprocessing_num_workers: 16 | |
### output | |
output_dir: saves/llama3-8b/lora/sft_bf16_p2_full_r2 | |
logging_steps: 10 | |
save_steps: 175 | |
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: 4.0 | |
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: epoch | |
eval_steps: 1 | |
report_to: wandb | |
run_name: llama3_8b_p2_full_r2 # optional | |