> Hi, everyone. Sorry for that, we updated the new runing command to fix it. Checking out [here](https://github.com/PKU-YuanGroup/MoE-LLaVA/blob/main/scripts/v1/qwen/finetune_moe.sh)

[hxhcreate]

          > Hi, everyone. Sorry for that, we updated the new runing command to fix it. Checking out [here](https://github.com/PKU-YuanGroup/MoE-LLaVA/blob/main/scripts/v1/qwen/finetune_moe.sh)

I still have the following error

AssertionError: The model has moe layers, but None of the param groups are marked as MoE. Create a param group with 'moe' key set to True before creating optimizer

here is my command

torchrun $DISTRIBUTED_ARGS moellava/train/train_mem.py \
    --moe_enable True --num_experts ${num_experts} --top_k_experts ${top_k_experts} --capacity_factor 1.5 \
    --moe_mode ${moe_mode} --use_residual ${use_residual} --router_aux_loss_coef ${router_aux_loss_coef} \
    --train_modules mlp.w1 mlp.w2 mlp.c_proj wg \
    --deepspeed ./scripts/zero2.json \
    --model_name_or_path ./checkpoints/llavaqwen1.5-1.8b-finetune \
    --version qwen \
    --data_path ${JSON_FOLDER}/llava_image_tune_.json ${JSON_FOLDER}/nlp_tune.json \
    --image_folder ${IMAGE_FOLDER} \
    --image_tower openai/clip-vit-large-patch14-336 \
    --image_projector_type mlp2x_gelu \
    --mm_vision_select_layer -2 \
    --mm_use_im_start_end False \
    --mm_use_im_patch_token False \
    --image_aspect_ratio pad \
    --group_by_modality_length True \
    --bf16 True \
    --output_dir ./checkpoints/llavaqwen-1.8b-finetune-moe \
    --num_train_epochs 1 \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 4 \
    --gradient_accumulation_steps 2 \
    --evaluation_strategy "no" \
    --save_strategy "steps" \
    --save_steps 24000 \
    --save_total_limit 1 \
    --learning_rate 2e-5 \
    --weight_decay 0. \
    --warmup_ratio 0.03 \
    --lr_scheduler_type "cosine" \
    --logging_steps 1 \
    --tf32 True \
    --model_max_length 2048 \
    --gradient_checkpointing True \
    --dataloader_num_workers 4 \
    --lazy_preprocess True \
    --report_to tensorboard \
    --cache_dir "./cache_dir"

here is my package version

accelerate                0.21.0
deepspeed                 0.9.5
torch                     2.0.1
torchvision               0.15.2
transformers              4.37.0

Originally posted by @hxhcreate in https://github.com/PKU-YuanGroup/MoE-LLaVA/issues/17#issuecomment-1958588177

[LinB203]

If you use qwen1.5, passing --train_modules mlp.gate_proj mlp.up_proj mlp.down_proj wg \.

[hxhcreate]

Thanks, work for me! Wonderful work