diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..a490b61
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,5 @@
+{
+    "githubPullRequests.ignoredPullRequestBranches": [
+        "main"
+    ]
+}
\ No newline at end of file
diff --git a/finetune/finetune.py b/finetune/finetune.py
index c0200a4..bf94c3d 100644
--- a/finetune/finetune.py
+++ b/finetune/finetune.py
@@ -19,7 +19,7 @@ from transformers import AutoModel, AutoTokenizer
 from dataset import SupervisedDataset, data_collator
 from trainer import CPMTrainer
 
-from peft import LoraConfig, get_peft_model
+from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
 
 @dataclass
 class ModelArguments:
@@ -220,7 +220,13 @@ def train():
     local_rank = training_args.local_rank
     world_size = int(os.environ.get("WORLD_SIZE", 1))
     ddp = world_size != 1
-    device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)} if ddp else None
+    device_map = None
+    if lora_args.q_lora:
+        device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)} if ddp else None
+        if len(training_args.fsdp) > 0 or deepspeed.is_deepspeed_zero3_enabled():
+            logging.warning(
+                "FSDP or ZeRO3 are not incompatible with QLoRA."
+            )
     
     model = AutoModel.from_pretrained(
         model_args.model_name_or_path,
@@ -258,11 +264,15 @@ def train():
             def get_input_embeddings(self):
                 return self.llm.get_input_embeddings()
             model.get_input_embeddings = MethodType(get_input_embeddings, model)
+        if lora_args.q_lora:
+            model = prepare_model_for_kbit_training(
+                model, use_gradient_checkpointing=training_args.gradient_checkpointing
+            )
         model = get_peft_model(model, lora_config)
-        model.base_model.llm.model.embed_tokens.weight.requires_grad_(True)
+        model.base_model.resampler.requires_grad_(True)
         if training_args.tune_vision:
             model.base_model.vpm.requires_grad_(True)
-            model.base_model.resampler.requires_grad_(True)
+            model.base_model.llm.model.embed_tokens.weight.requires_grad_(True)
         if training_args.gradient_checkpointing:
             model.enable_input_require_grads()
 
diff --git a/finetune/finetune_ds.sh b/finetune/finetune_ds.sh
index 716239a..2a86e6b 100644
--- a/finetune/finetune_ds.sh
+++ b/finetune/finetune_ds.sh
@@ -28,10 +28,10 @@ torchrun $DISTRIBUTED_ARGS finetune.py  \
     --remove_unused_columns false \
     --label_names "labels" \
     --prediction_loss_only false \
-    --bf16 true \
-    --bf16_full_eval true \
-    --fp16 false \
-    --fp16_full_eval false \
+    --bf16 false \
+    --bf16_full_eval false \
+    --fp16 true \
+    --fp16_full_eval true \
     --do_train \
     --do_eval \
     --tune_vision true \
diff --git a/finetune/finetune_lora.sh b/finetune/finetune_lora.sh
index b05b0f9..25d61c7 100644
--- a/finetune/finetune_lora.sh
+++ b/finetune/finetune_lora.sh
@@ -28,8 +28,10 @@ torchrun $DISTRIBUTED_ARGS finetune.py  \
     --remove_unused_columns false \
     --label_names "labels" \
     --prediction_loss_only false \
-    --bf16 true \
-    --bf16_full_eval true \
+    --bf16 false \
+    --bf16_full_eval false \
+    --fp16 true \
+    --fp16_full_eval true \
     --do_train \
     --do_eval \
     --tune_vision true \
diff --git a/finetune/readme.md b/finetune/readme.md
index a962622..4945404 100644
--- a/finetune/readme.md
+++ b/finetune/readme.md
@@ -93,12 +93,12 @@ model = AutoPeftModelForCausalLM.from_pretrained(
 
 ### Model Fine-tuning Memory Usage Statistics
 
-The following table presents the memory usage of the model when fine-tuning using NVIDIA A100 (80GiB) GPUs under different numbers of GPUs. The fine-tuning was performed with the DeepSpeed Zero-2 optimization and Gradient Checkpointing techniques, with a maximum length set to 2048 and batch size set to 1.
+The following table presents the memory usage of the model when fine-tuning using NVIDIA A100 (80GiB) GPUs under different numbers of GPUs. The fine-tuning was performed with the DeepSpeed Zero-3 optimization, Gradient Checkpointing techniques and offloading optimizer as well as parameters memory to cpu, with a maximum length set to 2048 and batch size set to 1. You refer to [deepspeed zero stage](https://huggingface.co/docs/transformers/v4.41.2/en/deepspeed#select-a-zero-stage) to reduce memory cost.
 
 | Fine-tuning Method | GPUs: 2 | GPUs: 4 | GPUs: 8 |
 |--------------------|---------|---------|---------|
-| LoRA Fine-tuning   | 31.2 GiB| 29.3 GiB|    28.4GiB   |
-| Full Parameters Fine-tuning | Out of memory | 75.0 GiB | 51.2GiB |
+| LoRA Fine-tuning   | 14.4 GiB| 13.6 GiB|   13.1 GiB   |
+| Full Parameters Fine-tuning | 16.0 GiB | 15.8 GiB | 15.63GiB |
 
 ### Notes
 - **Fine-tuning Method**: Displays two different fine-tuning strategies, LoRA fine-tuning and Full parameters fine-tuning.