wehub-resource-sync
34 lines
1.0 KiB
Bash
34 lines
1.0 KiB
Bash
# Use `--target_modules all-linear embed_tokens lm_head`
|
|
# Please adjust the `lm_head` according to the model.
|
|
nproc_per_node=2
|
|
|
|
CUDA_VISIBLE_DEVICES=0,1 \
|
|
NPROC_PER_NODE=$nproc_per_node \
|
|
swift sft \
|
|
--model Qwen/Qwen2.5-1.5B \
|
|
--tuner_type lora \
|
|
--dataset 'AI-ModelScope/alpaca-gpt4-data-zh#500' \
|
|
'AI-ModelScope/alpaca-gpt4-data-en#500' \
|
|
'swift/self-cognition' \
|
|
--torch_dtype bfloat16 \
|
|
--num_train_epochs 10 \
|
|
--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 embed_tokens lm_head \
|
|
--gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
|
|
--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 \
|
|
--deepspeed zero2
|