Delete finetune_lora.sh

finetune_lora.sh

-#!/bin/bash
-HIP_VISIBLE_DEVICES=0,1,2,3
-
-GPUS_PER_NODE=4
-NNODES=1
-NODE_RANK=0
-MASTER_ADDR=localhost
-MASTER_PORT=29500
-
-MODEL="/home/wanglch/projects/MiniCPM-V/MiniCPM-Llama3-V-2_5-base" # or openbmb/MiniCPM-V-2
-# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
-# See the section for finetuning in README for more information.
-DATA="/home/wanglch/projects/MiniCPM-V/data/self_build/train_data/train_data.json"
-EVAL_DATA="/home/wanglch/projects/MiniCPM-V/data/self_build/eval_data/eval_data.json"
-LLM_TYPE="llama3" # if use openbmb/MiniCPM-V-2, please set LLM_TYPE=minicpm
-
-DISTRIBUTED_ARGS="
-    --nproc_per_node $GPUS_PER_NODE \
-    --nnodes $NNODES \
-    --node_rank $NODE_RANK \
-    --master_addr $MASTER_ADDR \
-    --master_port $MASTER_PORT
-"
-torchrun $DISTRIBUTED_ARGS ./finetune/finetune.py  \
-    --model_name_or_path $MODEL \
-    --llm_type $LLM_TYPE \
-    --data_path $DATA \
-    --eval_data_path $EVAL_DATA \
-    --remove_unused_columns false \
-    --label_names "labels" \
-    --prediction_loss_only false \
-    --bf16 false \
-    --bf16_full_eval false \
-    --fp16 true \
-    --fp16_full_eval true \
-    --do_train \
-    --do_eval \
-    --tune_vision true \
-    --tune_llm false \
-    --use_lora true \
-    --lora_target_modules "llm\..*layers\.\d+\.self_attn\.(q_proj|k_proj)" \
-    --model_max_length 2048 \
-    --max_slice_nums 9 \
-    --max_steps 100 \
-    --eval_steps 10 \
-    --output_dir /home/wanglch/projects/saves/MiniCPM-Llama3-V-2_5/lora_train_dtk \
-    --logging_dir /home/wanglch/projects/saves/MiniCPM-Llama3-V-2_5/lora_train_dtk \
-    --logging_strategy "steps" \
-    --per_device_train_batch_size 2 \
-    --per_device_eval_batch_size 1 \
-    --gradient_accumulation_steps 1 \
-    --evaluation_strategy "steps" \
-    --save_strategy "steps" \
-    --save_steps 100 \
-    --save_total_limit 10 \
-    --learning_rate 1e-6 \
-    --weight_decay 0.1 \
-    --adam_beta2 0.95 \
-    --warmup_ratio 0.01 \
-    --lr_scheduler_type "cosine" \
-    --logging_steps 1 \
-    --gradient_checkpointing true \
-    --deepspeed ds_config_zero2.json \
-    --report_to "tensorboard" # wandb