diff --git a/toolkit/optimizers/adafactor.py b/toolkit/optimizers/adafactor.py
index 3f4a738c..b98f4590 100644
--- a/toolkit/optimizers/adafactor.py
+++ b/toolkit/optimizers/adafactor.py
@@ -2,6 +2,7 @@ import math
 from typing import List
 import torch
 from toolkit.optimizers.optimizer_utils import copy_stochastic, stochastic_grad_accummulation
+from optimum.quanto import QBytesTensor
 
 
 class Adafactor(torch.optim.Optimizer):
@@ -251,6 +252,9 @@ class Adafactor(torch.optim.Optimizer):
                         state["exp_avg_sq"] = state["exp_avg_sq"].to(grad)
 
                 p_data_fp32 = p
+                
+                if isinstance(p_data_fp32, QBytesTensor):
+                    p_data_fp32 = p_data_fp32.dequantize()
                 if p.dtype != torch.float32:
                     p_data_fp32 = p_data_fp32.clone().float()
 
diff --git a/toolkit/optimizers/optimizer_utils.py b/toolkit/optimizers/optimizer_utils.py
index f895244c..28ae280d 100644
--- a/toolkit/optimizers/optimizer_utils.py
+++ b/toolkit/optimizers/optimizer_utils.py
@@ -1,6 +1,96 @@
 import torch
 from torch import Tensor
 from typing import Optional
+from optimum.quanto import QBytesTensor
+
+
+def compute_scale_for_dtype(tensor, dtype):
+    """
+    Compute appropriate scale for the given tensor and target dtype.
+    
+    Args:
+        tensor: Input tensor to be quantized
+        dtype: Target dtype for quantization
+    Returns:
+        Appropriate scale factor for the quantization
+    """
+    if dtype == torch.int8:
+        abs_max = torch.max(torch.abs(tensor))
+        return abs_max / 127.0 if abs_max > 0 else 1.0
+    elif dtype == torch.uint8:
+        max_val = torch.max(tensor)
+        min_val = torch.min(tensor)
+        range_val = max_val - min_val
+        return range_val / 255.0 if range_val > 0 else 1.0
+    elif dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
+        # For float8, we typically want to preserve the magnitude of the values
+        # while fitting within the representable range of the format
+        abs_max = torch.max(torch.abs(tensor))
+        if dtype == torch.float8_e4m3fn:
+            # e4m3fn has range [-448, 448] with no infinities
+            max_representable = 448.0
+        else:  # torch.float8_e5m2
+            # e5m2 has range [-57344, 57344] with infinities
+            max_representable = 57344.0
+        
+        return abs_max / max_representable if abs_max > 0 else 1.0
+    else:
+        raise ValueError(f"Unsupported dtype for quantization: {dtype}")
+
+def quantize_tensor(tensor, dtype):
+    """
+    Quantize a floating-point tensor to the target dtype with appropriate scaling.
+    
+    Args:
+        tensor: Input tensor (float)
+        dtype: Target dtype for quantization
+    Returns:
+        quantized_data: Quantized tensor
+        scale: Scale factor used
+    """
+    scale = compute_scale_for_dtype(tensor, dtype)
+    
+    if dtype == torch.int8:
+        quantized_data = torch.clamp(torch.round(tensor / scale), -128, 127).to(dtype)
+    elif dtype == torch.uint8:
+        quantized_data = torch.clamp(torch.round(tensor / scale), 0, 255).to(dtype)
+    elif dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
+        # For float8, we scale and then cast directly to the target type
+        # The casting operation will handle the appropriate rounding
+        scaled_tensor = tensor / scale
+        quantized_data = scaled_tensor.to(dtype)
+    else:
+        raise ValueError(f"Unsupported dtype for quantization: {dtype}")
+        
+    return quantized_data, scale
+
+
+def update_parameter(target, result_float):
+    """
+    Updates a parameter tensor, handling both regular torch.Tensor and QBytesTensor cases
+    with proper rescaling for quantized tensors.
+    
+    Args:
+        target: The parameter to update (either torch.Tensor or QBytesTensor)
+        result_float: The new values to assign (torch.Tensor)
+    """
+    if isinstance(target, QBytesTensor):
+        # Get the target dtype from the existing quantized tensor
+        target_dtype = target._data.dtype
+        
+        # Handle device placement
+        device = target._data.device
+        result_float = result_float.to(device)
+        
+        # Compute new quantized values and scale
+        quantized_data, new_scale = quantize_tensor(result_float, target_dtype)
+        
+        # Update the internal tensors with newly computed values
+        target._data.copy_(quantized_data)
+        target._scale.copy_(new_scale)
+    else:
+        # Regular tensor update
+        target.copy_(result_float)
 
 
 def get_format_params(dtype: torch.dtype) -> tuple[int, int]:
@@ -98,7 +188,9 @@ def copy_stochastic(
         elif target.dtype == torch.float8_e5m2:
             result_float.clamp_(-57344.0, 57344.0)
 
-        target.copy_(result_float)
+        # Copy the result to the target tensor
+        update_parameter(target, result_float)
+        # target.copy_(result_float)
         del result, rand, source_int