diff --git a/training/scripts/train.py b/training/scripts/train.py
index 423afd3..e393685 100755
--- a/training/scripts/train.py
+++ b/training/scripts/train.py
@@ -98,19 +98,35 @@ def train(config_path):
                     except Exception as e:
                         print(f"✗ Failed: {e}")
                         
-                        # Strategy 5: bf16 to CPU
-                        print("\n[5/6] Trying: bf16 to CPU...")
+                        # Strategy 5: bf16 with accelerate CPU offload
+                        print("\n[5/6] Trying: bf16 with accelerate CPU offload...")
                         try:
-                            model = AutoModelForCausalLM.from_pretrained(config["base_model"], torch_dtype=torch.bfloat16, device_map="cpu", low_cpu_mem_usage=True, trust_remote_code=True)
-                            print("✓ Success: bf16 to CPU")
+                            from accelerate import Accelerator
+                            model = AutoModelForCausalLM.from_pretrained(
+                                config["base_model"],
+                                torch_dtype=torch.bfloat16,
+                                device_map="cpu",
+                                low_cpu_mem_usage=True,
+                                trust_remote_code=True,
+                            )
+                            accelerator = Accelerator(cpu_offload=True)
+                            model = accelerator.prepare(model)
+                            print("✓ Success: bf16 with accelerate CPU offload")
                         except Exception as e:
                             print(f"✗ Failed: {e}")
                             
-                            # Strategy 6: bf16 auto
-                            print("\n[6/6] Trying: bf16 auto...")
+                            # Strategy 6: bf16 to CPU with gradient checkpointing
+                            print("\n[6/6] Trying: bf16 to CPU with gradient checkpointing...")
                             try:
-                                model = AutoModelForCausalLM.from_pretrained(config["base_model"], torch_dtype=torch.bfloat16, device_map="auto", low_cpu_mem_usage=True, trust_remote_code=True)
-                                print("✓ Success: bf16 auto")
+                                model = AutoModelForCausalLM.from_pretrained(
+                                    config["base_model"],
+                                    torch_dtype=torch.bfloat16,
+                                    device_map="cpu",
+                                    low_cpu_mem_usage=True,
+                                    trust_remote_code=True,
+                                )
+                                model.gradient_checkpointing_enable()
+                                print("✓ Success: bf16 to CPU with gradient checkpointing")
                             except Exception as e:
                                 print(f"✗ Failed: {e}")
                                 raise RuntimeError("All loading strategies failed!")