diff --git a/language_interpolation/state_space_network.py b/language_interpolation/state_space_network.py
index 0c5dc4a..e12e398 100644
--- a/language_interpolation/state_space_network.py
+++ b/language_interpolation/state_space_network.py
@@ -30,6 +30,7 @@
 import torch.nn.functional as F
 from dataclasses import dataclass
 from einops import rearrange, repeat, einsum
+from typing import Union
 
 
 @dataclass
@@ -93,55 +94,6 @@ class MambaLMHeadModel, https://github.com/state-spaces/mamba/blob/main/mamba_ss
 
         return logits
 
-    
-    @staticmethod
-    def from_pretrained(pretrained_model_name: str):
-        """Load pretrained weights from HuggingFace into model.
-    
-        Args:
-            pretrained_model_name: One of
-                * 'state-spaces/mamba-2.8b-slimpj'
-                * 'state-spaces/mamba-2.8b'
-                * 'state-spaces/mamba-1.4b'
-                * 'state-spaces/mamba-790m'
-                * 'state-spaces/mamba-370m'
-                * 'state-spaces/mamba-130m'
-                            
-        Returns:
-            model: Mamba model with weights loaded
-    
-        """
-        from transformers.utils import WEIGHTS_NAME, CONFIG_NAME
-        from transformers.utils.hub import cached_file
-        
-        def load_config_hf(model_name):
-            resolved_archive_file = cached_file(model_name, CONFIG_NAME,
-                                                _raise_exceptions_for_missing_entries=False)
-            return json.load(open(resolved_archive_file))
-        
-        
-        def load_state_dict_hf(model_name, device=None, dtype=None):
-            resolved_archive_file = cached_file(model_name, WEIGHTS_NAME,
-                                                _raise_exceptions_for_missing_entries=False)
-            return torch.load(resolved_archive_file, weights_only=True)
-        
-        config_data = load_config_hf(pretrained_model_name)
-        args = ModelArgs(
-            d_model=config_data['d_model'],
-            n_layer=config_data['n_layer'],
-            vocab_size=config_data['vocab_size']
-        )
-        model = Mamba(args)
-        
-        state_dict = load_state_dict_hf(pretrained_model_name)
-        new_state_dict = {}
-        for key in state_dict:
-            new_key = key.replace('backbone.', '')
-            new_state_dict[new_key] = state_dict[key]
-        model.load_state_dict(new_state_dict)
-        
-        return model
-
 
 class ResidualBlock(nn.Module):
     def __init__(self, args: ModelArgs):