hello，I am very happy to see that fastfold has added the multimer function, but I have a problem. When using the monomer function, I still cannot predict 5 models at once. Is there a solution for this?
here is my scripts：

#!/bin/bash
#DSUB --job_type cosched
#DSUB -n fastfold
#DSUB -A root.bingxing2.gpuuser001
#DSUB -q root.default
#DSUB -R 'cpu=12;gpu=2;mem=90000'
#DSUB -l wuhanG5500
#DSUB -N 1
#DSUB -e %J.out
#DSUB -o %J.out
######################查看gpu利用率################################################
STATE_FILE="state_${BATCH_JOB_ID}"
/usr/bin/touch ${STATE_FILE}
function gpus_collection(){
while [[ cat "${STATE_FILE}" | grep "over" | wc -l == "0" ]]; do
/usr/bin/sleep 1
/usr/bin/nvidia-smi >> "gpu_${BATCH_JOB_ID}.log"
done
}
gpus_collection &
#####################AF2计算部分###################################################
module load anaconda/2021.11
module load cuda/11.3.0-gcc-4.8.5-oaa
module load gcc/9.3.0-gcc-4.8.5-bxl
source activate fastfold
af2Root=/home/bingxing2/public

add '--gpus [N]' to use N gpus for inference

add '--enable_workflow' to use parallel workflow for data processing

add '--use_precomputed_alignments [path_to_alignments]' to use precomputed msa

add '--chunk_size [N]' to use chunk to reduce peak memory

add '--inplace' to use inplace to save memory

python inference.py mono.fasta $af2Root/alphafold2.2.0/pdb_mmcif/mmcif_files
--output_dir ./mono_out
--uniref90_database_path $af2Root/uniref90/uniref90.fasta
--mgnify_database_path $af2Root/mgnify/mgy_clusters.fa
--pdb70_database_path $af2Root/pdb70/pdb70
--param_path $af2Root/alphafold2.2.0/params/params_model_1.npz
--uniclust30_database_path $af2Root/uniclust30/uniclust30_2018_08/uniclust30_2018_08
--bfd_database_path $af2Root/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt
--jackhmmer_binary_path which jackhmmer
--hhblits_binary_path which hhblits
--hhsearch_binary_path which hhsearch
--kalign_binary_path which kalign
--gpus 2
--enable_workflow
--chunk_size 1
--inplace
echo "over" >> "${STATE_FILE}"