update sampler and use LoRA finetune LLM

I'm trying to use finetune_lora.sh to finetune InternLM-XComposer2. Except for using LoRA to finetune the part of LLM, I want to know whether can I also update the parameter of sampler. Changing the ` --fix_sampler False` is the only thing I have to do? Or I should change somwthing else?
For the state of saving updated model, I notice that they only save the part of LoRA. Maybe I need to do some change for `safe_save_model_for_hf_trainer` or `get_peft_state_maybe_zero_3`?

note: LoRA finetune LLM and update sampler(projection layer)

If anyone can give some advice I would be very grateful.

#!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=1
DIR=`pwd`

export MODEL="internlm/internlm-xcomposer2d5-7b"
export DATA="data.txt"

GPUS_PER_NODE=8
NNODES=1
NODE_RANK=0
MASTER_ADDR=localhost
MASTER_PORT=6001

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.py \
--model_name_or_path $MODEL \
--data_path $DATA \
--given_num True \
--bf16 True \
--fix_vit True \
**--fix_sampler False \**
--use_lora True \
--hd_num 18 \
--output_dir output/finetune_lora \
--num_train_epochs 1 \
--batch_size 2 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--evaluation_strategy "no" \
--save_strategy "epoch" \
--save_total_limit 1 \
--learning_rate 5e-5 \
--weight_decay 0.1 \
--adam_beta2 0.95 \
--warmup_ratio 0.01 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--report_to "none" \
--max_length 16384 \
--deepspeed ds_config_zero2.json \
--gradient_checkpointing True