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如何加载预训练的lora,在这个lora的基础上继续进行训练
一阶段训练脚本,得到预训练的lora,二次训练的脚本怎么写呢 CUDA_VISIBLE_DEVICES=0 swift sft --model Qwen/Qwen2.5-7B-Instruct --train_type lora --dataset 'AI-ModelScope/alpaca-gpt4-data-zh#500' 'AI-ModelScope/alpaca-gpt4-data-en#500' 'swift/self-cognition#500' --torch_dtype bfloat16 --num_train_epochs 1 --per_device_train_batch_size 1 --per_device_eval_batch_size 1 --learning_rate 1e-4 --lora_rank 8 --lora_alpha 32 --target_modules all-linear --gradient_accumulation_steps 16 --eval_steps 50 --save_steps 50 --save_total_limit 2 --logging_steps 5 --max_length 2048 --output_dir output --system 'You are a helpful assistant.' --warmup_ratio 0.05 --dataloader_num_workers 4 --model_author swift --model_name swift-robot
The text was updated successfully, but these errors were encountered:
设置 --resume_from_checkpoint ckpt_path --resume_only_model true
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如何加载预训练的lora,在这个lora的基础上继续进行训练
一阶段训练脚本,得到预训练的lora,二次训练的脚本怎么写呢
CUDA_VISIBLE_DEVICES=0
swift sft
--model Qwen/Qwen2.5-7B-Instruct
--train_type lora
--dataset 'AI-ModelScope/alpaca-gpt4-data-zh#500'
'AI-ModelScope/alpaca-gpt4-data-en#500'
'swift/self-cognition#500'
--torch_dtype bfloat16
--num_train_epochs 1
--per_device_train_batch_size 1
--per_device_eval_batch_size 1
--learning_rate 1e-4
--lora_rank 8
--lora_alpha 32
--target_modules all-linear
--gradient_accumulation_steps 16
--eval_steps 50
--save_steps 50
--save_total_limit 2
--logging_steps 5
--max_length 2048
--output_dir output
--system 'You are a helpful assistant.'
--warmup_ratio 0.05
--dataloader_num_workers 4
--model_author swift
--model_name swift-robot
The text was updated successfully, but these errors were encountered: