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https://github.com/OpenBMB/MiniCPM-V.git
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55 lines
1.5 KiB
Bash
55 lines
1.5 KiB
Bash
#!/bin/bash
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GPUS_PER_NODE=8
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NNODES=1
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NODE_RANK=0
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MASTER_ADDR=localhost
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MASTER_PORT=6001
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MODEL="path/to/minicpmv2"
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# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
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# See the section for finetuning in README for more information.
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DATA="path/to/trainging_data"
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EVAL_DATA="path/to/test_data"
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DISTRIBUTED_ARGS="
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--nproc_per_node $GPUS_PER_NODE \
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--nnodes $NNODES \
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--node_rank $NODE_RANK \
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--master_addr $MASTER_ADDR \
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--master_port $MASTER_PORT
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"
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torchrun $DISTRIBUTED_ARGS finetune.py \
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--model_name_or_path $MODEL \
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--data_path $DATA \
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--eval_data_path $EVAL_DATA \
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--remove_unused_columns false \
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--label_names "labels" \
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--prediction_loss_only false \
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--bf16 true \
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--bf16_full_eval true \
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--do_train \
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--do_eval \
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--model_max_length 2048 \
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--max_steps 80000 \
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--eval_steps 200 \
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--output_dir output/output_minicpmv2 \
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--logging_dir output/output_minicpmv2 \
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--logging_strategy "steps" \
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--per_device_train_batch_size 8 \
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--per_device_eval_batch_size 1 \
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--gradient_accumulation_steps 1 \
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--evaluation_strategy "steps" \
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--save_strategy "steps" \
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--save_steps 1000 \
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--save_total_limit 10 \
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--learning_rate 5e-7 \
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--weight_decay 0.1 \
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--adam_beta2 0.95 \
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--warmup_ratio 0.01 \
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--lr_scheduler_type "cosine" \
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--logging_steps 1 \
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--gradient_checkpointing True \
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--deepspeed ds_config_zero2.json \
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--report_to "tensorboard" # wandb
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