diff --git a/src/instructlab/dolomite/hf_models/model_conversion/llama.py b/src/instructlab/dolomite/hf_models/model_conversion/llama.py
index cf028ab..e8e6754 100644
--- a/src/instructlab/dolomite/hf_models/model_conversion/llama.py
+++ b/src/instructlab/dolomite/hf_models/model_conversion/llama.py
@@ -229,6 +229,9 @@ def export_to_huggingface_llama(
         config.num_key_value_heads,
         config.n_embd // config.n_head,
         AttentionHeadType(config.attention_head_type),
+        m_emb=config.m_emb,
+        m_residual=config.m_residual,
+        # m_width=config.m_width,
     )
 
     SafeTensorsWeightsManager.save_state_dict(state_dict, save_path)
@@ -285,11 +288,25 @@ def _export_state_dict_to_huggingface(
     num_key_value_heads: int,
     head_dim: int,
     attention_head_type: AttentionHeadType,
+    m_residual: float = None, 
+    m_emb: float = None,
+    m_width: float = None,
 ) -> None:
+    if m_residual is None:
+        m_residual = 1.
+    if m_emb is None:
+        m_emb = 1.
+
+    # NOTE: this will not work since the norms are tied
+    # has_m_width = False
+    # if m_width is None:
+    #     has_m_width = True
+    #     m_width = 1.
+    
     state_dict = {
         "model.embed_tokens.weight": safetensors_weight_manager.get_tensor(
             "transformer.wte.weight"
-        ),
+        ) * m_emb,
         "model.norm.weight": safetensors_weight_manager.get_tensor(
             "transformer.ln_f.weight"
         ),
@@ -298,7 +315,12 @@ def _export_state_dict_to_huggingface(
     if safetensors_weight_manager.has_tensor("lm_head.weight"):
         state_dict["lm_head.weight"] = safetensors_weight_manager.get_tensor(
             "lm_head.weight"
-        )
+        ) / m_width
+    # elif has_m_width:
+    #     # int this we cannot tie
+    #     state_dict["lm_head.weight"] = safetensors_weight_manager.get_tensor(
+    #         "transformer.wte.weight"
+    #     ) / m_width
 
     for layer_idx in range(num_layers):
         state_dict[f"model.layers.{layer_idx}.input_layernorm.weight"] = (
@@ -332,13 +354,13 @@ def _export_state_dict_to_huggingface(
         state_dict[f"model.layers.{layer_idx}.mlp.down_proj.weight"] = (
             safetensors_weight_manager.get_tensor(
                 f"transformer.h.{layer_idx}.mlp.c_proj.weight"
-            )
+            ) * m_residual
         )
         if f"transformer.h.{layer_idx}.mlp.c_proj.bias" in safetensors_weight_manager:
             state_dict[f"model.layers.{layer_idx}.mlp.down_proj.bias"] = (
                 safetensors_weight_manager.get_tensor(
                     f"transformer.h.{layer_idx}.mlp.c_proj.bias"
-                )
+                ) * m_residual
             )
 
         query_weight, key_weight, value_weight = (
@@ -376,12 +398,12 @@ def _export_state_dict_to_huggingface(
             safetensors_weight_manager.get_tensor(
                 f"transformer.h.{layer_idx}.attn.c_proj.weight"
             )
-        )
+        ) * m_residual
         if f"transformer.h.{layer_idx}.attn.c_proj.bias" in safetensors_weight_manager:
             state_dict[f"model.layers.{layer_idx}.self_attn.o_proj.bias"] = (
                 safetensors_weight_manager.get_tensor(
                     f"transformer.h.{layer_idx}.attn.c_proj.bias"
                 )
-            )
+            ) * m_residual
 
     return state_dict