
CUDA_VISIBLE_DEVICES="0" python main.py \
    --model_name_or_path huggyllama/llama-7b \
    --output_dir ./output/edge_llm_llama_7b \
    --qat True \
    --uniform_bits 8 \
    --w_bits 4 \
    --a_bits 4 \
    --kv_bits 4 \
    --pruning_scheme "unstructure" \
    --logging_steps 10 \
    --save_strategy steps \
    --pruning_device 'cuda' \
    --save_pruned_model True \
    --pruned_model_path "./pruning/pruned_models/" \
    --save_steps 1000 \
    --eval_steps 500 \
    --save_total_limit 40 \
    --evaluation_strategy steps \
    --eval_dataset_size 1024 \
    --max_eval_samples 1000 \
    --per_device_eval_batch_size 1 \
    --eval_batch_size 4 \
    --dataloader_num_workers 3 \
    --group_by_length \
    --logging_strategy steps \
    --remove_unused_columns False \
    --do_train \
    --do_eval \
    --do_wikitext2_ppl \
    --lora_r 8 \
    --lora_alpha 16 \
    --bf16 \
    --layer_wise_pruning True \
    --pruning True \
    --mse_file "./pruning/layer_wise_MSE_results/" \
    --total_pruning_ratio 0.5 \
    --warmup_ratio 0.03 \
    --gradient_checkpointing False \
    --dataset alpaca \
    --source_max_len 1024 \
    --target_max_len 256 \
    --per_device_train_batch_size 1 \
    --gradient_accumulation_steps 16 \
    --report_to wandb \
    --max_steps 10000 \
    --learning_rate 0.0004 \
    --adam_beta2 0.999 \
    --max_grad_norm 0.3 \
    --lora_dropout 0.1 \
    --weight_decay 0.0 \
