diff --git a/convert_grok.py b/convert_grok.py
index d71d647dd..7d0dbfc77 100644
--- a/convert_grok.py
+++ b/convert_grok.py
@@ -204,7 +204,7 @@ def dump_state_dict(f, ggml_type, input_dir, config):
     # First operate on meta tensors to find shapes and dtypes for GGUF header.
     for idx, name in enumerate(weight_names):
         weight, scales = get_weights(f"{input_dir}/tensor{idx:05}_000")
-        meta_tensor = convert_weight(name, weight, scales, config.experts, device="meta")
+        meta_tensor = convert_weight(name, weight, scales, config, device="meta")
         dtype, tensor_ggml_type = get_dtype_and_ggml_type(meta_tensor, ggml_type)
         quantized_meta_tensor = maybe_quantize_tensor(meta_tensor, tensor_ggml_type)
         f.add_tensor_info(
@@ -226,8 +226,7 @@ def dump_state_dict(f, ggml_type, input_dir, config):
 
     for name in weight_names:
         weight, scales = weights.pop(name)
-        tensor = convert_weight(name, weight, scales, config.experts)
-        tensor = maybe_permute_tensor(name, tensor, config)
+        tensor = convert_weight(name, weight, scales, config)
         _, tensor_ggml_type = get_dtype_and_ggml_type(tensor, ggml_type)
         array = maybe_quantize_tensor(tensor, tensor_ggml_type).numpy()
 
@@ -258,7 +257,7 @@ def from_numpy(array):
     return torch.from_numpy(array)
 
 
-def convert_weight(name, weight, scales, experts, dtype=torch.float32, device=None):
+def convert_weight(name, weight, scales, config, dtype=torch.float32, device=None):
     # copied from https://gist.github.com/chu-tianxiang/ec310e15d56949fd0f351cb5f65ee7a1
     weight = from_numpy(weight).to(device=device, dtype=dtype)
     if scales is not None:
@@ -271,32 +270,19 @@ def convert_weight(name, weight, scales, experts, dtype=torch.float32, device=No
         else:
             weight = weight * scale
 
-    # Transpose linear matrix
-    if len(weight.shape) >= 2 and "token_embd" not in name:
+    if name == "token_embd":
+        weight *= config.embedding_multiplier_scale
+    elif len(weight.shape) >= 2:
+        # Transpose linear matrix
         weight = weight.transpose(-1, -2)
 
+
     if name.endswith("ffn_gate_inp") or name.endswith("_exps"):
-        weight = weight[experts]  # gather.
+        weight = weight[config.experts]  # gather.
 
     return weight
 
 
-def maybe_permute_tensor(name, tensor, config):
-    def permute(weights, n_head):
-        return (
-            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-            .swapaxes(1, 2)
-            .reshape(weights.shape)
-        )
-
-    if name.endswith("attn_k"):
-        return permute(tensor, config.num_key_value_heads)
-    elif name.endswith("attn_q"):
-        return permute(tensor, config.num_attention_heads)
-
-    return tensor
-
-
 def extract_vocabulary_from_model(vocab):
     tokens = []
     scores = []