diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py
index 3c0d5012aed..10f0a4515aa 100644
--- a/unsloth/models/llama.py
+++ b/unsloth/models/llama.py
@@ -16,7 +16,7 @@
 import gc
 import math
 import functools
-from typing import Optional, Tuple, List, Union
+from typing import Any, Dict, Optional, Tuple, List, Union
 from ._utils import *
 from ._utils import patch_unsloth_smart_gradient_checkpointing
 from ._utils import __version__
@@ -113,6 +113,46 @@ def original_apply_o(self, X):
 # SDPA has GQA internally
 SDPA_HAS_GQA = "enable_gqa" in scaled_dot_product_attention.__doc__
 
+
+def _prepare_model_for_qat(model: torch.nn.Module, qat_scheme: str) -> torch.nn.Module:
+    """
+    Apply QAT + LoRA during fine-tuning.
+
+    On a high level, this means fake quantizing the base (frozen) model during LoRA training.
+    Fake quantization refers to simulating quantization numerics in high precision (e.g. bf16).
+    This helps mitigate quantization degradations when the model is quantized after training.
+
+    For more details: https://dev-discuss.pytorch.org/t/speeding-up-qat-by-1-89x-with-lora/2700
+    """
+    try:
+        from torchao.quantization import (
+            Float8DynamicActivationFloat8WeightConfig,
+            Float8DynamicActivationInt4WeightConfig,
+            PerRow,
+            quantize_,
+        )
+        from torchao.quantization.qat import QATConfig
+    except ImportError as e:
+        print(
+            "Please install torchao nightly for the latest QAT features:\n"
+            "  pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126"
+        )
+        raise e
+    pass
+    filter_fn = None
+    if qat_scheme == "fp8-int4":
+        group_size = 128
+        base_config = Float8DynamicActivationInt4WeightConfig(group_size=group_size)
+        filter_fn = lambda m, _: isinstance(m, torch.nn.Linear) and m.in_features >= group_size
+    elif qat_scheme == "fp8-fp8":
+        base_config = Float8DynamicActivationFloat8WeightConfig(granularity=PerRow())
+    else:
+        raise ValueError(f"Unexpected QAT scheme {qat_scheme}")
+    pass
+    quantize_(model, QATConfig(base_config, step="prepare"), filter_fn=filter_fn)
+    return model
+pass
+
 # Fix new HF's inference code
 def _fast_prepare_inputs_for_generation(self, input_ids, attention_mask=None, **kwargs,):
     past_key_values = kwargs.get("past_key_values", None)
@@ -2234,6 +2274,7 @@ def get_peft_model(
         init_lora_weights   = True,
         loftq_config        = {},
         temporary_location  = "_unsloth_temporary_saved_buffers",
+        qat_scheme          = None,
         **kwargs,
     ):
         if os.environ.get("UNSLOTH_USE_NEW_MODEL", "0") == "1":
@@ -2600,6 +2641,12 @@ def get_peft_model(
 
         model = _get_peft_model(model, lora_config)
 
+        # Apply QAT + LoRA if specified
+        if qat_scheme is not None:
+            print("Unsloth: Applying QAT to mitigate quantization degradation")
+            model = _prepare_model_for_qat(model, qat_scheme)
+        pass
+
         model._saved_temp_tokenizer = _saved_temp_tokenizer
 
         model = FastLlamaModel.patch_peft_model(model, use_gradient_checkpointing)