wehub-resource-sync
42 lines
1.3 KiB
Bash
42 lines
1.3 KiB
Bash
# 2 * 60GiB
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# mcore shell: https://github.com/modelscope/ms-swift/blob/main/examples/megatron/multimodal/omni/moe.sh
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NPROC_PER_NODE=2 \
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IMAGE_MAX_TOKEN_NUM=1024 \
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VIDEO_MAX_TOKEN_NUM=128 \
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FPS_MAX_FRAMES=12 \
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CUDA_VISIBLE_DEVICES=0,1 \
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swift sft \
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--model Qwen/Qwen3-Omni-30B-A3B-Instruct \
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--dataset 'AI-ModelScope/alpaca-gpt4-data-zh#10000' \
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'AI-ModelScope/LaTeX_OCR:human_handwrite#5000' \
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'swift/VideoChatGPT:Generic#2000' \
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'speech_asr/speech_asr_aishell1_trainsets:validation#5000' \
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--split_dataset_ratio 0.01 \
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--load_from_cache_file true \
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--tuner_type lora \
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--torch_dtype bfloat16 \
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--num_train_epochs 1 \
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--per_device_train_batch_size 8 \
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--per_device_eval_batch_size 8 \
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--attn_impl flash_attn \
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--experts_impl grouped_mm \
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--learning_rate 1e-4 \
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--lora_rank 8 \
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--lora_alpha 32 \
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--target_modules all-linear \
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--freeze_vit true \
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--freeze_aligner true \
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--padding_free true \
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--gradient_accumulation_steps 1 \
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--gradient_checkpointing true \
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--eval_steps 50 \
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--save_steps 50 \
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--save_total_limit 2 \
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--logging_steps 5 \
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--max_length 4096 \
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--output_dir output \
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--warmup_ratio 0.05 \
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--dataset_num_proc 4 \
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--deepspeed zero3 \
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--dataloader_num_workers 4
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