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# Currently, it only supports the case where the model and reward_model use the same template/tokenizer.
# Currently, multimodal model PPO is not supported.
nproc_per_node=4
CUDA_VISIBLE_DEVICES=0,1,2,3 \
NPROC_PER_NODE=$nproc_per_node \
swift rlhf \
--rlhf_type ppo \
--model LLM-Research/Meta-Llama-3.1-8B-Instruct \
--reward_model 'AI-ModelScope/Skywork-Reward-Llama-3.1-8B-v0.2' \
--train_type lora \
--dataset 'AI-ModelScope/alpaca-gpt4-data-zh#20000' 'AI-ModelScope/alpaca-gpt4-data-en#20000' \
--torch_dtype bfloat16 \
--num_train_epochs 1 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--learning_rate 1e-5 \
--lora_rank 8 \
--lora_alpha 32 \
--target_modules all-linear \
--gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
--eval_steps 100 \
--save_steps 100 \
--save_total_limit 2 \
--logging_steps 5 \
--max_length 2048 \
--output_dir output \
--warmup_ratio 0.05 \
--dataloader_num_workers 4 \
--deepspeed zero2 \
--response_length 512 \
--temperature 0.7 \
--dataset_num_proc 4
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