diff --git a/train.py b/train.py
index a0d0a61..a2f7162 100644
--- a/train.py
+++ b/train.py
@@ -37,77 +37,71 @@ def train(config_path):
     errors = []
     
     # ------------------------------------------------------------------
-    # Strategy 1: QLoRA with FSDP (preferred)
+    # Strategy 1: 4-bit model AS-IS (already quantized)
     # ------------------------------------------------------------------
-    print("\n[1/4] Trying: 4-bit QLoRA (FSDP)...")
+    print("\n[1/4] Trying: 4-bit AS-IS (FSDP)...")
     try:
-        bnb_config = BitsAndBytesConfig(
-            load_in_4bit=True,
-            bnb_4bit_quant_type="nf4",
-            bnb_4bit_compute_dtype=torch.bfloat16,
-            bnb_4bit_use_double_quant=True,
-        )
         model = AutoModelForCausalLM.from_pretrained(
             config["base_model"],
-            quantization_config=bnb_config,
+            torch_dtype=torch.float16,
             device_map="auto",
             trust_remote_code=True,
             low_cpu_mem_usage=True,
         )
-        print("✓ Success: QLoRA 4-bit")
+        print("✓ Success: 4-bit AS-IS (FSDP)")
     except Exception as e:
-        errors.append(("QLoRA 4-bit", e))
+        errors.append(("4-bit AS-IS", e))
         print(f"✗ Failed: {e}")
         
         # --------------------------------------------------------------
-        # Strategy 2: BF16 GPU
+        # Strategy 2: 4-bit to CPU
         # --------------------------------------------------------------
-        print("\n[2/4] Trying: bf16 GPU...")
+        print("\n[2/4] Trying: 4-bit to CPU...")
         try:
             model = AutoModelForCausalLM.from_pretrained(
                 config["base_model"],
-                torch_dtype=torch.bfloat16,
-                device_map="auto",
+                torch_dtype=torch.float16,
+                device_map="cpu",
                 trust_remote_code=True,
                 low_cpu_mem_usage=True,
             )
-            print("✓ Success: bf16 GPU")
+            print("✓ Success: 4-bit to CPU")
         except Exception as e:
-            errors.append(("bf16 GPU", e))
+            errors.append(("4-bit CPU", e))
             print(f"✗ Failed: {e}")
             
             # ----------------------------------------------------------
-            # Strategy 3: BF16 CPU
+            # Strategy 3: bf16 auto
             # ----------------------------------------------------------
-            print("\n[3/4] Trying: bf16 CPU...")
+            print("\n[3/4] Trying: bf16 auto...")
             try:
                 model = AutoModelForCausalLM.from_pretrained(
                     config["base_model"],
                     torch_dtype=torch.bfloat16,
-                    device_map="cpu",
+                    device_map="auto",
                     trust_remote_code=True,
                     low_cpu_mem_usage=True,
                 )
-                print("✓ Success: bf16 CPU")
+                print("✓ Success: bf16 auto")
             except Exception as e:
-                errors.append(("bf16 CPU", e))
+                errors.append(("bf16 auto", e))
                 print(f"✗ Failed: {e}")
                 
                 # ------------------------------------------------------
-                # Strategy 4: FP16 GPU
+                # Strategy 4: bf16 CPU
                 # ------------------------------------------------------
-                print("\n[4/4] Trying: fp16 GPU...")
+                print("\n[4/4] Trying: bf16 CPU...")
                 try:
                     model = AutoModelForCausalLM.from_pretrained(
                         config["base_model"],
-                        torch_dtype=torch.float16,
-                        device_map="auto",
+                        torch_dtype=torch.bfloat16,
+                        device_map="cpu",
                         trust_remote_code=True,
                         low_cpu_mem_usage=True,
                     )
-                    print("✓ Success: fp16 GPU")
+                    print("✓ Success: bf16 CPU")
                 except Exception as e:
-                    errors.append(("fp16 GPU", e))
+                    errors.append(("bf16 CPU", e))
                     print(f"✗ Failed: {e}")
                     msg = "\n".join(
                         f"{name}: {err}" for name, err in errors
@@ -167,13 +161,14 @@ def train(config_path):
         eval_steps=config.get("eval_steps", 100),
         bf16=True,
         gradient_checkpointing=config.get("gradient_checkpointing", True),
-        fsdp=["full_shard", "auto_wrap"],
+        fsdp=True,
         fsdp_config={
             "backward_prefetch": "backward_pre",
             "forward_prefetch": "true",
             "cpu_offload": "false",
             "auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
             "transformer_layer_cls_to_wrap": "Qwen3_5MoeDecoderLayer",
+            "activation_checkpointing": True,
         },
     )
     
diff --git a/training/configs/ornith-35b-lora.yaml b/training/configs/ornith-35b-lora.yaml
index 718c801..fedab50 100644
--- a/training/configs/ornith-35b-lora.yaml
+++ b/training/configs/ornith-35b-lora.yaml
@@ -1,7 +1,7 @@
 # LoRA Training Configuration for Llama-2-7b
 # Dataset: cyron_summary_lora_dataset (20k examples)
 
-base_model: /data/models/Ornith-1.0-35B
+base_model: /data/models/Ornith-1.0-35B-4bit
 model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer