67 lines
1.6 KiB
Bash
Executable File
67 lines
1.6 KiB
Bash
Executable File
#!/bin/bash
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export CUDA_DEVICE_MAX_CONNECTIONS=1
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DIR=`pwd`
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MODEL="Qwen/Qwen-7B-Chat-Int4" # Set the path if you do not want to load from huggingface directly
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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_data"
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function usage() {
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echo '
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Usage: bash finetune/finetune_qlora_single_gpu.sh [-m MODEL_PATH] [-d DATA_PATH]
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'
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}
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while [[ "$1" != "" ]]; do
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case $1 in
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-m | --model )
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shift
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MODEL=$1
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;;
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-d | --data )
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shift
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DATA=$1
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;;
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-h | --help )
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usage
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exit 0
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;;
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* )
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echo "Unknown argument ${1}"
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exit 1
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;;
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esac
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shift
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done
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export CUDA_VISIBLE_DEVICES=0
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# Remember to use --fp16 instead of --bf16 due to autogptq
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python finetune.py \
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--model_name_or_path $MODEL \
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--data_path $DATA \
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--fp16 True \
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--output_dir output_qwen \
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--num_train_epochs 5 \
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--per_device_train_batch_size 2 \
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--per_device_eval_batch_size 1 \
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--gradient_accumulation_steps 8 \
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--evaluation_strategy "no" \
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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 3e-4 \
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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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--report_to "none" \
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--model_max_length 512 \
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--lazy_preprocess True \
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--gradient_checkpointing \
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--use_lora \
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--q_lora \
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--deepspeed finetune/ds_config_zero2.json
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