wehub-resource-sync
54 lines
2.0 KiB
Bash
54 lines
2.0 KiB
Bash
# 8*80G GPU
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# CHORD https://arxiv.org/abs/2508.11408
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# GRPO total batch = 32(prompts)*8(num_generations) = 256 = 8(gpus) * 4(per_device_train_batch_size) * 8(gradient_accumulation_steps)
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# SFT total batch = 64 = 8(gpus) * 1(chord_sft_per_device_train_batch_size) * 8(gradient_accumulation_steps)
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# NOTE: We use the same dataset for GRPO and SFT, which may cause overlap (i.e., the same examples to be selected).
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# You can pre-download the dataset and manually split it to avoid this.
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export CHORD_SYSTEM_PROMPT="You are a helpful assistant that solves MATH problems.
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You should first think about the reasoning process in mind and then provide the user with the answer.
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You should present your reasoning process using the format: <think>\n...your reasoning process here... </think>\n"
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CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
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NPROC_PER_NODE=8 \
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swift rlhf \
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--rlhf_type grpo \
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--model Qwen/Qwen2.5-7B-Instruct \
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--dataset AI-MO/NuminaMath-TIR \
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--load_from_cache_file true \
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--torch_dtype bfloat16 \
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--beta 0.0 \
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--num_train_epochs 1 \
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--per_device_train_batch_size 4 \
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--gradient_accumulation_steps 8 \
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--chord_sft_per_device_train_batch_size 1 \
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--chord_sft_dataset AI-MO/NuminaMath-TIR \
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--chord_enable_phi_function false \
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--chord_mu_warmup_steps 0 \
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--chord_mu_decay_steps 200 \
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--chord_mu_peak 0.9 \
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--chord_mu_valley 0.05 \
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--num_generations 8 \
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--tuner_type full \
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--reward_funcs accuracy \
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--system "$CHORD_SYSTEM_PROMPT" \
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--use_vllm true \
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--vllm_mode colocate \
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--vllm_gpu_memory_utilization 0.4 \
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--vllm_max_model_len 8192 \
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--max_completion_length 4096 \
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--overlong_filter true \
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--offload_optimizer true \
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--offload_model true \
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--sleep_level 1 \
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--save_steps 1000 \
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--learning_rate 1e-6 \
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--save_total_limit 2 \
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--logging_steps 1 \
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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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--log_completions true \
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--report_to tensorboard swanlab
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