wehub-resource-sync
38 lines
1.2 KiB
Bash
38 lines
1.2 KiB
Bash
# 8 * 65 GiB
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# Currently, it only supports the case where the model and reward_model use the same template/tokenizer.
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# Currently, multimodal model PPO is not supported.
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# pip install "deepspeed==0.14.*"
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nproc_per_node=8
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PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \
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CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
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NPROC_PER_NODE=$nproc_per_node \
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swift rlhf \
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--rlhf_type ppo \
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--model LLM-Research/Meta-Llama-3.1-8B-Instruct \
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--reward_model 'AI-ModelScope/Skywork-Reward-Llama-3.1-8B-v0.2' \
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--tuner_type full \
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--dataset 'AI-ModelScope/alpaca-gpt4-data-zh#20000' 'AI-ModelScope/alpaca-gpt4-data-en#20000' \
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--load_from_cache_file true \
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--split_dataset_ratio 0.01 \
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--torch_dtype bfloat16 \
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--num_train_epochs 1 \
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--per_device_train_batch_size 1 \
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--per_device_eval_batch_size 1 \
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--learning_rate 1e-6 \
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--gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
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--eval_steps 100 \
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--save_steps 100 \
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--save_total_limit 2 \
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--logging_steps 5 \
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--max_length 2048 \
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--output_dir output \
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--warmup_ratio 0.05 \
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--dataloader_num_workers 4 \
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--deepspeed zero3 \
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--response_length 512 \
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--temperature 0.7 \
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--dataset_num_proc 4 \
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--save_only_model true
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