diff --git a/experiments/demo_magtrain_llm_sft.sh b/experiments/demo_magtrain_llm_sft.sh
index 68af07ba..521a4f51 100644
--- a/experiments/demo_magtrain_llm_sft.sh
+++ b/experiments/demo_magtrain_llm_sft.sh
@@ -14,9 +14,9 @@ source ${SCRIPTPATH}/wandb.sh
 echo $SLURM_TMPDIR
 export TMPDIR="/cache"
 
-export TRITON_CACHE_DIR=${HOME}/.cache/triton
-export HF_DATASETS_CACHE=${HOME}/.cache/dataset
-export HF_HOME=${HOME}/.cache/huggingface
+export TRITON_CACHE_DIR=${HOME}/project/cache/triton
+export HF_DATASETS_CACHE=${HOME}/project/cache/dataset
+export HF_HOME=${HOME}/project/cache/huggingface
 
 # TORCH and NCCL
 export CUDA_LAUNCH_BLOCKING=1
diff --git a/experiments/demo_magtrain_slurm.sh b/experiments/demo_magtrain_slurm.sh
index 3b6a9505..1e2ebf55 100644
--- a/experiments/demo_magtrain_slurm.sh
+++ b/experiments/demo_magtrain_slurm.sh
@@ -24,8 +24,18 @@ source ${SCRIPTPATH}/util.sh
 
 CONTAINER=${HOME}/container/pt2402.sif
 
-srun --jobid $SLURM_JOB_ID apptainer exec -B $SLURM_TMPDIR:/cache  --nv $CONTAINER bash ${SCRIPTPATH}/demo_magtrain_llm_sft.sh
+# srun --jobid $SLURM_JOB_ID apptainer exec -B $SLURM_TMPDIR:/cache  --nv $CONTAINER bash ${SCRIPTPATH}/demo_magtrain_llm_sft.sh
 
 # use nsys to profile training process
-# srun --jobid $SLURM_JOB_ID apptainer exec -B $SLURM_TMPDIR:/cache --nv --fakeroot $CONTAINER nsys profile -t cuda,nvtx -o /cache/nsys_${SLURM_JOB_ID} bash ${SCRIPTPATH}/demo_magtrain_llm_sft.sh
-# cp $SLURM_TMPDIR/nsys_${SLURM_JOB_ID}.nsys-rep ${HOME}/project/log/nsys/
+srun --jobid $SLURM_JOB_ID \
+    apptainer exec -B $SLURM_TMPDIR:/cache --nv --fakeroot $CONTAINER \
+    nsys profile  -s none -t cuda,nvtx \
+    --gpu-metrics-device=all \
+    --gpu-metrics-frequency=100 \
+    --nic-metrics=true \
+    --capture-range=cudaProfilerApi \
+    --capture-range-end=stop \
+    -o /cache/nsys_${SLURM_JOB_ID} \
+    bash ${SCRIPTPATH}/demo_magtrain_llm_sft.sh
+
+cp $SLURM_TMPDIR/nsys_${SLURM_JOB_ID}.nsys-rep ${HOME}/project/log/nsys/
diff --git a/recipes/llama3-8b/sft/config_full.yaml b/recipes/llama3-8b/sft/config_full.yaml
index 0efa95bb..6076304f 100644
--- a/recipes/llama3-8b/sft/config_full.yaml
+++ b/recipes/llama3-8b/sft/config_full.yaml
@@ -32,13 +32,15 @@ optim_target_modules: all-linear
 weight_decay: 0.01
 lr_scheduler_type: cosine
 max_seq_length: 8192
+packing: false
+dataset_num_proc: 16
 max_steps: -1
 num_train_epochs: 3
 output_dir: /home/l069561/project/alignment-handbook/experiments/models/llama-3-full-ultrachat
 overwrite_output_dir: true
 per_device_eval_batch_size: 1
-per_device_train_batch_size: 1
-gradient_accumulation_steps: 64
+per_device_train_batch_size: 1 # this is per device, you need to manual calculate global batch by per device * gas * gpu * node
+gradient_accumulation_steps: 4
 push_to_hub: false
 remove_unused_columns: true
 report_to:
diff --git a/scripts/run_sft.py b/scripts/run_sft.py
index 3efb3013..b795e9ab 100644
--- a/scripts/run_sft.py
+++ b/scripts/run_sft.py
@@ -219,9 +219,7 @@ def main():
             eval_dataset=eval_dataset,
             dataset_text_field="text",
             dataset_num_proc=data_args.preprocessing_num_workers,
-            max_seq_length=training_args.max_seq_length,
             tokenizer=tokenizer,
-            packing=True,
             dataset_kwargs=training_args.dataset_kwargs,
             callbacks=[GpuUtilPrintCallBack()],
         )
@@ -234,9 +232,7 @@ def main():
             eval_dataset=eval_dataset,
             dataset_text_field="text",
             dataset_num_proc=data_args.preprocessing_num_workers,
-            max_seq_length=training_args.max_seq_length,
             tokenizer=tokenizer,
-            packing=True,
             peft_config=get_peft_config(model_args),
             dataset_kwargs=training_args.dataset_kwargs,
             callbacks=[GpuUtilPrintCallBack()],
diff --git a/src/alignment/configs.py b/src/alignment/configs.py
index e85c4ec6..a0e59270 100644
--- a/src/alignment/configs.py
+++ b/src/alignment/configs.py
@@ -18,9 +18,11 @@
 from dataclasses import dataclass, field
 from typing import Any, Dict, List, NewType, Optional, Union
 
-import trl
 from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, HfArgumentParser
 
+import trl
+
+
 MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())
 MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
 
@@ -214,7 +216,7 @@ class ModelArguments:
         default="uint8",
         metadata={"help": "storage type to pack the quanitzed 4-bit prarams."},
     )
-    use_flash_attention_2: bool = field(default=False)
+    # use_flash_attention_2: bool = field(default=False)
 
     def __post_init__(self):
         if self.load_in_8bit and self.load_in_4bit:
@@ -292,6 +294,10 @@ class SFTConfig(trl.SFTConfig):
             "help": ("Whether to log and evaluate the first global_step or not.")
         },
     )
+    # max_seq_length: Optional[int] = field(
+    #     default=None,
+    # )
+    # packing: Optional[bool] = field(default=False)
 
 
 @dataclass