diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..85c8fed
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+alpaca_lora/
+repository/
+__pycache__/
diff --git a/Finetune4bConfig.py b/Finetune4bConfig.py
index c074028..3514060 100644
--- a/Finetune4bConfig.py
+++ b/Finetune4bConfig.py
@@ -1,3 +1,4 @@
+import os
 class Finetune4bConfig:
     """Config holder for LLaMA 4bit finetuning
     """
@@ -64,6 +65,12 @@ class Finetune4bConfig:
         self.logging_steps = logging_steps
         self.checkpoint = checkpoint
         self.skip = skip
+        self.world_size = int(os.environ.get("WORLD_SIZE", 1))
+        self.local_rank = int(os.environ.get("LOCAL_RANK", 0))
+        self.ddp = self.world_size != 1
+        self.device_map = "auto" if not self.ddp else {"": self.local_rank}
+        if self.ddp:
+            self.gradient_accumulation_steps = self.gradient_accumulation_steps // self.world_size
 
 
     def __str__(self) -> str:
@@ -74,5 +81,6 @@ class Finetune4bConfig:
         f"{self.gradient_checkpointing=}\n{self.gradient_checkpointing_ratio=}\n" +\
         f"{self.warmup_steps=}\n{self.save_steps=}\n{self.save_total_limit=}\n" +\
         f"{self.logging_steps=}\n" +\
-        f"{self.checkpoint=}\n{self.skip=}"
+        f"{self.checkpoint=}\n{self.skip=}\n" +\
+        f"{self.world_size=}\n{self.ddp=}\n{self.device_map=}"
         return s.replace("self.", "")
diff --git a/GPTQ-for-LLaMa/autograd_4bit.py b/GPTQ-for-LLaMa/autograd_4bit.py
index c55b7d3..ac90789 100644
--- a/GPTQ-for-LLaMa/autograd_4bit.py
+++ b/GPTQ-for-LLaMa/autograd_4bit.py
@@ -15,6 +15,8 @@ auto_switch_thd = 16
 def get_buffer(shape_of_qweight, dtype=torch.float16, device='cuda'):
     if shape_of_qweight not in buffer_mat_dic.keys():
         buffer_mat_dic[shape_of_qweight] = torch.zeros((shape_of_qweight[0] * 8, shape_of_qweight[1]), dtype=dtype, device=device)
+    elif buffer_mat_dic[shape_of_qweight].device != device:
+        buffer_mat_dic[shape_of_qweight] = buffer_mat_dic[shape_of_qweight].to(device)
     return buffer_mat_dic[shape_of_qweight]
     
 
@@ -195,7 +197,7 @@ def model_to_float(model):
     print('Converted as Float.')
 
 
-def load_llama_model_4bit_low_ram(config_path, model_path, half=False):
+def load_llama_model_4bit_low_ram(config_path, model_path, half=False, device_map="auto"):
     import transformers
     import accelerate
     from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
@@ -217,8 +219,13 @@ def load_llama_model_4bit_low_ram(config_path, model_path, half=False):
             if name in layers:
                 del layers[name]
         make_quant_for_4bit_autograd(model, layers)
-    model = accelerate.load_checkpoint_and_dispatch(model=model, checkpoint=model_path, device_map='auto')
-    model.cuda()
+    model = accelerate.load_checkpoint_and_dispatch(
+        model=model,
+        checkpoint=model_path,
+        device_map=device_map,
+        no_split_module_classes=["LlamaDecoderLayer"]
+    )
+
     model.seqlen = 2048
     
     if half:
diff --git a/finetune.py b/finetune.py
index 6224f2e..3f8821e 100644
--- a/finetune.py
+++ b/finetune.py
@@ -38,13 +38,14 @@ import train_data
 ft_config = get_config()
 
 # * Show loaded parameters
-print(f"{ft_config}\n")
+if ft_config.local_rank == 0:
+    print(f"{ft_config}\n")
 
 if ft_config.gradient_checkpointing:
     print('Disable Dropout.')
 
 # Load Basic Model
-model, tokenizer = load_llama_model_4bit_low_ram(ft_config.llama_q4_config_dir, ft_config.llama_q4_model)
+model, tokenizer = load_llama_model_4bit_low_ram(ft_config.llama_q4_config_dir, ft_config.llama_q4_model, device_map=ft_config.device_map)
 
 # Config Lora
 lora_config = LoraConfig(
@@ -92,6 +93,11 @@ if not ft_config.skip:
         from gradient_checkpointing import apply_gradient_checkpointing
         apply_gradient_checkpointing(model, checkpoint_ratio=ft_config.gradient_checkpointing_ratio)
 
+    # Disable Trainer's DataParallel for multigpu
+    if not ft_config.ddp and torch.cuda.device_count() > 1:
+        model.is_parallelizable = True
+        model.model_parallel = True
+
     trainer = transformers.Trainer(
         model=model,
         train_dataset=data.train_data,
@@ -110,7 +116,8 @@ if not ft_config.skip:
             save_steps=ft_config.save_steps,
             output_dir=ft_config.lora_out_dir,
             save_total_limit=ft_config.save_total_limit,
-            load_best_model_at_end=False
+            load_best_model_at_end=False,
+            ddp_find_unused_parameters=False if ft_config.ddp else None,
         ),
         data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
     )