diff --git a/mobilevlm/model/mobilevlm.py b/mobilevlm/model/mobilevlm.py
index 307f78c..e6af382 100644
--- a/mobilevlm/model/mobilevlm.py
+++ b/mobilevlm/model/mobilevlm.py
@@ -64,6 +64,9 @@ def initialize_vision_modules(self, model_args, fsdp=None):
         # Build Vision-Projector
         if getattr(self, 'mm_projector', None) is None:
             self.mm_projector = build_vision_projector(self.config)
+        # In case it is frozen by LoRA
+        for p in self.mm_projector.parameters():
+            p.requires_grad = True
         if pretrain_mm_mlp_adapter is not None:
             mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
             def get_w(weights, keyword):
diff --git a/mobilevlm/train/train.py b/mobilevlm/train/train.py
index 53f69ee..9e9d1bb 100644
--- a/mobilevlm/train/train.py
+++ b/mobilevlm/train/train.py
@@ -142,7 +142,11 @@ def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
 def find_all_linear_names(model):
     cls = torch.nn.Linear
     lora_module_names = set()
+    multimodal_keywords = ['mm_projector', 'vision_tower', 'vision_resampler']
+
     for name, module in model.named_modules():
+        if any(mm_keyword in name for mm_keyword in multimodal_keywords):
+            continue
         if isinstance(module, cls):
             names = name.split('.')
             lora_module_names.add(names[0] if len(names) == 1 else names[-1])
@@ -854,7 +858,8 @@ def make_inputs_require_grad(module, input, output):
         model.config.tune_mm_mlp_adapter = training_args.tune_mm_mlp_adapter = model_args.tune_mm_mlp_adapter
         model.config.vision_tower_type = training_args.vision_tower_type = model_args.vision_tower_type
 
-        model.requires_grad_(True)
+        if not training_args.lora_enable:
+            model.requires_grad_(True)
 
         if model_args.tune_mm_mlp_adapter:
             model.requires_grad_(False)
@@ -910,6 +915,7 @@ def make_inputs_require_grad(module, input, output):
         if training_args.local_rank == 0 or training_args.local_rank == -1:
             model.config.save_pretrained(training_args.output_dir)
             model.save_pretrained(training_args.output_dir, state_dict=state_dict)
+            print('non_lora_trainable...', non_lora_state_dict.keys())
             torch.save(non_lora_state_dict, os.path.join(training_args.output_dir, 'non_lora_trainables.bin'))
     else:
         safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir)
diff --git a/run.sh b/run.sh
index 2301606..569f509 100644
--- a/run.sh
+++ b/run.sh
@@ -113,6 +113,57 @@ case ${TASK} in
         cd ${WORK_DIR}
         OUTPUT_DIR=$3
         bash scripts/benchmark.sh ${OUTPUT_DIR}
+    ;;    
+    "finetune.lora")
+        echo ">>> Start Visual-Instruction Tuning with LoRA..."
+        cd ${WORK_DIR}
+        LANGUAGE_MODEL=$3
+        VISION_MODEL=$4
+        OUTPUT_DIR=$5
+        OUTPUT_DIR_PT=${OUTPUT_DIR}/mobilevlm_v2-1.pretrain
+        OUTPUT_DIR_FT=${OUTPUT_DIR}/mobilevlm_v2-2.finetune-lora
+        mkdir -p ${OUTPUT_DIR_FT}
+        declare -A DS_CONF
+        deepspeed mobilevlm/train/train_mem.py \
+            --deepspeed scripts/deepspeed/zero3.json \
+            --lora_enable True --lora_r 128 --lora_alpha 256 \
+            --learning_rate 2e-4 \
+            --model_name_or_path ${OUTPUT_DIR_PT} \
+            --version v1 \
+            --data_path data/finetune_data/MobileVLM_V2_FT_Mix2M.json \
+            --image_folder data/finetune_data \
+            --vision_tower ${VISION_MODEL} \
+            --vision_tower_type clip \
+            --mm_projector_type ldpnetv2 \
+            --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 ${OUTPUT_DIR_FT} \
+            --num_train_epochs 1 \
+            --per_device_train_batch_size 16 \
+            --per_device_eval_batch_size 4 \
+            --gradient_accumulation_steps 1 \
+            --evaluation_strategy "no" \
+            --save_strategy "steps" \
+            --save_steps 50000 \
+            --save_total_limit 1 \
+            --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 none \
+            2>&1 | tee -a ${OUTPUT_DIR_FT}/log.txt &&
+        python3 scripts/mergelora.py ${OUTPUT_DIR_PT} ${OUTPUT_DIR}/mobilevlm_v2-2.finetune-lora ${OUTPUT_DIR}/mobilevlm_v2-2.finetune \
+            2>&1 | tee -a ${OUTPUT_DIR_FT}/log.txt &&
+        echo "Done."
     ;;
     *)
         echo "error with ${DATASET_ID}"
diff --git a/run_v1.sh b/run_v1.sh
index cf1de12..3650f77 100644
--- a/run_v1.sh
+++ b/run_v1.sh
@@ -130,10 +130,12 @@ case ${TASK} in
         mkdir -p ${OUTPUT_DIR_FT}
         declare -A DS_CONF
         DS_CONF=([mobilevlm1.7b]=zero2 [mobilevlm3b]=zero3)
+        declare -A LR_CONF
+        LR_CONF=([mobilevlm1.7b]=1e-4 [mobilevlm3b]=2e-4)
         deepspeed mobilevlm/train/train_mem.py \
             --deepspeed scripts/deepspeed/${DS_CONF[${ARCH}]}.json \
             --lora_enable True --lora_r 128 --lora_alpha 256 \
-            --learning_rate 2e-4 \
+            --learning_rate ${LR_CONF[${ARCH}]} \
             --model_name_or_path ${LANGUAGE_MODEL} \
             --version v1 \
             --data_path data/finetune_data/llava_v1_5_mix665k.json \