diff --git a/transformer_engine/jax/praxis/module.py b/transformer_engine/jax/praxis/module.py
index b82c0915e4..e33ff39385 100644
--- a/transformer_engine/jax/praxis/module.py
+++ b/transformer_engine/jax/praxis/module.py
@@ -6,6 +6,7 @@
 """
 from functools import partial
 from typing import Callable, Iterable, Sequence, Tuple, Union
+from dataclasses import field, dataclass
 
 from praxis import pax_fiddle
 from praxis.base_layer import init_var
@@ -27,6 +28,7 @@ def _generate_ln_scale_init(scale_init):
     return scale_init
 
 
+@dataclass
 class TransformerEngineBaseLayer(BaseLayer):
     """TransformerEngineBaseLayer"""
 
@@ -66,6 +68,7 @@ def create_layer(self, name, flax_module_cls):
         self.create_child(name, flax_module_p.clone())
 
 
+@dataclass
 class LayerNorm(TransformerEngineBaseLayer):
     """LayerNorm"""
 
@@ -74,7 +77,7 @@ class LayerNorm(TransformerEngineBaseLayer):
     zero_centered_gamma: bool = False
     scale_init: WeightInit = None
     scale_axes: Tuple[str, ...] = ()
-    bias_init: WeightInit = WeightInit.Constant(0.0)
+    bias_init: WeightInit = field(default_factory=WeightInit.Constant(0.0))
     bias_axes: Tuple[str, ...] = ()
     transpose_batch_sequence: bool = False
 
@@ -102,6 +105,7 @@ def __call__(self, x: JTensor) -> JTensor:
         return self.layer_norm(x)
 
 
+@dataclass
 class FusedSoftmax(TransformerEngineBaseLayer):
     """FusedSoftmax"""
 
@@ -123,13 +127,14 @@ def __call__(self, x: JTensor, mask: JTensor = None, bias: JTensor = None) -> JT
         return self.fused_softmax(x, mask, bias)
 
 
+@dataclass
 class Linear(TransformerEngineBaseLayer):
     """Linear"""
 
     out_features: int = 512
     kernel_axes: Tuple[str, ...] = ()
     use_bias: bool = True
-    bias_init: WeightInit = WeightInit.Constant(0.0)
+    bias_init: WeightInit = field(default_factory=WeightInit.Constant(0.0))
     bias_axes: Tuple[str, ...] = ()
     enable_low_rank_adaptation: bool = False
     low_rank_adaptation_dim: int = 32
@@ -164,6 +169,7 @@ def __call__(self, x: JTensor) -> JTensor:
         return self.linear(x)
 
 
+@dataclass
 class LayerNormLinear(TransformerEngineBaseLayer):
     """LayerNormLinear"""
 
@@ -174,11 +180,11 @@ class LayerNormLinear(TransformerEngineBaseLayer):
     zero_centered_gamma: bool = False
     scale_init: WeightInit = None
     scale_axes: Tuple[str, ...] = ()
-    ln_bias_init: WeightInit = WeightInit.Constant(1.0)
+    ln_bias_init: WeightInit = field(default_factory=WeightInit.Constant(1.0))
     ln_bias_axes: Tuple[str, ...] = ()
     kernel_axes: Tuple[str, ...] = ()
     use_bias: bool = False
-    bias_init: WeightInit = WeightInit.Constant(0.0)
+    bias_init: WeightInit = field(default_factory=WeightInit.Constant(0.0))
     bias_axes: Tuple[str, ...] = ()
     enable_low_rank_adaptation: bool = False
     low_rank_adaptation_dim: int = 32
@@ -227,6 +233,7 @@ def __call__(self, x: JTensor) -> JTensor:
         return self.ln_linear(x)
 
 
+@dataclass
 class LayerNormMLP(TransformerEngineBaseLayer):
     """LayerNormMLP"""
 
@@ -237,12 +244,12 @@ class LayerNormMLP(TransformerEngineBaseLayer):
     zero_centered_gamma: bool = False
     scale_init: WeightInit = None
     scale_axes: Tuple[str, ...] = ()
-    ln_bias_init: WeightInit = WeightInit.Constant(1.0)
+    ln_bias_init: WeightInit = field(default_factory=WeightInit.Constant(1.0))
     ln_bias_axes: Tuple[str, ...] = ()
     kernel_axes_1: Tuple[str, ...] = ()
     kernel_axes_2: Tuple[str, ...] = ()
     use_bias: bool = False
-    bias_init: WeightInit = WeightInit.Constant(0.0)
+    bias_init: WeightInit = field(default_factory=WeightInit.Constant(0.0))
     bias_axes_1: Tuple[str, ...] = ()
     bias_axes_2: Tuple[str, ...] = ()
     enable_low_rank_adaptation: bool = False
diff --git a/transformer_engine/jax/praxis/transformer.py b/transformer_engine/jax/praxis/transformer.py
index f2ac802f10..405362f4f8 100644
--- a/transformer_engine/jax/praxis/transformer.py
+++ b/transformer_engine/jax/praxis/transformer.py
@@ -6,6 +6,7 @@
 """
 from functools import partial
 from typing import Optional, Sequence, Tuple
+from dataclasses import field, dataclass
 import warnings
 
 from praxis import pax_fiddle
@@ -21,6 +22,7 @@
 from ..attention import AttnBiasType, AttnMaskType
 
 
+@dataclass
 class RelativePositionBiases(TransformerEngineBaseLayer):
     """RelativePositionBiases"""
 
@@ -36,7 +38,7 @@ def generate_embedding_init(init, num_attention_heads, num_buckets):
         embedding_init = init
         if embedding_init is None:
             rb_stddev = (num_attention_heads * num_buckets) ** -0.5
-            embedding_init = WeightInit.Gaussian(rb_stddev)
+            embedding_init = field(default_factory=WeightInit.Gaussian(rb_stddev))
         return embedding_init
 
     def setup(self) -> None:
@@ -66,6 +68,7 @@ def __call__(self, q_seqlen: JTensor, k_seqlen: JTensor, bidirectional: bool = T
         return self.relative_position_bias(q_seqlen, k_seqlen, bidirectional)
 
 
+@dataclass
 class DotProductAttention(TransformerEngineBaseLayer):
     """DotProductAttention"""
 
@@ -124,6 +127,7 @@ def __call__(
         )
 
 
+@dataclass
 class MultiHeadAttention(TransformerEngineBaseLayer):
     """MultiHeadAttention"""
 
@@ -138,7 +142,7 @@ class MultiHeadAttention(TransformerEngineBaseLayer):
     zero_centered_gamma: bool = False
     return_layernorm_output: bool = False
     use_bias: bool = False
-    bias_init: WeightInit = WeightInit.Constant(0.0)
+    bias_init: WeightInit = field(default_factory=WeightInit.Constant(0.0))
     attn_mask_type: str = "causal"
     attn_bias_type: Optional[str] = None
     enable_rotary_pos_emb: bool = False
@@ -257,6 +261,7 @@ def __call__(
         )
 
 
+@dataclass
 class TransformerLayer(TransformerEngineBaseLayer):
     """TransformerLayer"""
 
@@ -275,7 +280,7 @@ class TransformerLayer(TransformerEngineBaseLayer):
     dropout_rng_name: str = "dropout"
     mlp_activations: Sequence[str] = ("relu",)
     use_bias: bool = False
-    bias_init: WeightInit = WeightInit.Constant(0.0)
+    bias_init: WeightInit = field(default_factory=WeightInit.Constant(0.0))
     apply_residual_connection_post_layernorm: bool = False
     output_layernorm: bool = False
     float32_attention_logits: bool = False