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# Tested with 2 & 4 GPUs
set -x
if [ "$#" -lt 2 ]; then
echo "Usage: run_qwen_05_peft.sh <nproc_per_node> <save_path> [other_configs...]"
exit 1
fi
nproc_per_node=$1
save_path=$2
# Shift the arguments so $@ refers to the rest
shift 2
torchrun --standalone --nnodes=1 --nproc_per_node=$nproc_per_node \
-m verl.trainer.fsdp_sft_trainer \
data.train_files=$HOME/data/gsm8k/train.parquet \
data.val_files=$HOME/data/gsm8k/test.parquet \
data.prompt_key=extra_info \
data.response_key=extra_info \
optim.lr=1e-4 \
+data.prompt_dict_keys=['question'] \
+data.response_dict_keys=['answer'] \
data.micro_batch_size_per_gpu=4 \
model.partial_pretrain=Qwen/Qwen2.5-0.5B-Instruct \
trainer.default_local_dir=$save_path \
trainer.project_name=gsm8k-sft \
trainer.experiment_name=gsm8k-sft-qwen-2.5-0.5b-instruct \
trainer.logger=['console'] \
trainer.total_epochs=1 \
trainer.default_hdfs_dir=null $@ \
model.lora_rank=32\
model.lora_alpha=16 \
model.target_modules=all-linear
# Or you can do this:
# model.target_modules=[q_proj,v_proj] \
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