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finetune/finetune_qlora_single.sh

+#!/bin/bash
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+DIR=`pwd`
+
+MODEL="Qwen/Qwen-VL-Chat-Int4" # Qwen/Qwen-VL-Chat-Int4 Set the path if you do not want to load from huggingface directly
+# 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="path_to_data"
+
+export CUDA_VISIBLE_DEVICES=0
+
+# Remember to use --fp16 instead of --bf16 due to autogptq
+python finetune.py \
+    --model_name_or_path $MODEL \
+    --data_path $DATA \
+    --fp16 True \
+    --fix_vit True \
+    --output_dir output_qwen \
+    --num_train_epochs 5 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --gradient_accumulation_steps 8 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 1000 \
+    --save_total_limit 10 \
+    --learning_rate 1e-5 \
+    --weight_decay 0.1 \
+    --adam_beta2 0.95 \
+    --warmup_ratio 0.01 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --report_to "none" \
+    --model_max_length 2048 \
+    --lazy_preprocess True \
+    --gradient_checkpointing \
+    --use_lora \
+    --q_lora \
+    --deepspeed finetune/ds_config_zero2.json