LLaMA-Factory/examples/mllm/sft_instructblip.sh

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#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage sft_mm \
--do_train \
--model_name_or_path /home/LAB/fengzc/LLM/checkpoints/Salesforce/instructblip-vicuna-7b \
--dataset llava_instruct_100 \
--dataset_dir data \
--template default \
--finetuning_type lora \
--lora_target q_proj,k_proj \
--output_dir saves/instructblip-vicuna-7b/lora/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 4 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 1 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 1e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--plot_loss \
--quantization_bit 8 \
--image_path /home/LAB/fengzc/LLM/checkpoints/liuhaotian/LLaVA-Instruct-150K/images/coco/train2017 \
--use_qformer