diff --git a/transformer_lens/HookedTransformer.py b/transformer_lens/HookedTransformer.py
index baf32ad05..0a0ae69b9 100644
--- a/transformer_lens/HookedTransformer.py
+++ b/transformer_lens/HookedTransformer.py
@@ -1038,7 +1038,7 @@ def move_model_modules_to_device(self):
     @classmethod
     def from_pretrained(
         cls,
-        model_name: str,
+        model_name: Union[str, None],
         fold_ln: bool = True,
         center_writing_weights: bool = True,
         center_unembed: bool = True,
@@ -1076,10 +1076,10 @@ def from_pretrained(
         Loaded pretrained model tiny-stories-1M into HookedTransformer
 
         Args:
-            model_name: The model name - must be an element of
+            model_name: The model name - if a string, must be an element of
                 :const:`transformer_lens.loading_from_pretrained.OFFICIAL_MODEL_NAMES` or an alias
                 of one. The full list of available models can be found in the docs under :doc:`model
-                properties</generated/model_properties_table>`.
+                properties</generated/model_properties_table>`. If None, you must provide hf_model. 
             fold_ln: Whether to fold in the LayerNorm weights to the
                 subsequent linear layer. This does not change the computation.
 
@@ -1195,6 +1195,8 @@ def from_pretrained(
             or from_pretrained_kwargs.get("load_in_4bit", False)
         ), "Quantization not supported"
 
+        assert hf_model is not None or model_name is not None, "Must specify model_name or hf_model."
+        
         if hf_model is not None:
             hf_cfg = hf_model.config.to_dict()
             qc = hf_cfg.get("quantization_config", {})
@@ -1206,7 +1208,7 @@ def from_pretrained(
                 load_in_4bit and (version.parse(torch.__version__) < version.parse("2.1.1"))
             ), "Quantization is only supported for torch versions >= 2.1.1"
             assert not (
-                load_in_4bit and ("llama" not in model_name.lower())
+                load_in_4bit and not isinstance(hf_cfg, transformers.models.llama.configuration_llama.LlamaConfig)
             ), "Quantization is only supported for Llama models"
             if load_in_4bit:
                 assert (
@@ -1229,9 +1231,12 @@ def from_pretrained(
         ) and device in ["cpu", None]:
             logging.warning("float16 models may not work on CPU. Consider using a GPU or bfloat16.")
 
-        # Get the model name used in HuggingFace, rather than the alias.
-        official_model_name = loading.get_official_model_name(model_name)
-
+        if model_name is not None:
+           # Get the model name used in HuggingFace, rather than the alias.
+           official_model_name = loading.get_official_model_name(model_name)
+        else:
+            official_model_name = None
+        
         # Load the config into an HookedTransformerConfig object. If loading from a
         # checkpoint, the config object will contain the information about the
         # checkpoint
@@ -1294,7 +1299,7 @@ def from_pretrained(
         if move_to_device:
             model.move_model_modules_to_device()
 
-        print(f"Loaded pretrained model {model_name} into HookedTransformer")
+        print(f"Loaded pretrained model with {model_name=} into HookedTransformer")
 
         return model
 
diff --git a/transformer_lens/loading_from_pretrained.py b/transformer_lens/loading_from_pretrained.py
index f8d9d2c19..f093e1574 100644
--- a/transformer_lens/loading_from_pretrained.py
+++ b/transformer_lens/loading_from_pretrained.py
@@ -1375,7 +1375,7 @@ def get_checkpoint_labels(model_name: str, **kwargs):
 
 # %% Loading state dicts
 def get_pretrained_state_dict(
-    official_model_name: str,
+    official_model_name: str | None,
     cfg: HookedTransformerConfig,
     hf_model=None,
     dtype: torch.dtype = torch.float32,
@@ -1395,8 +1395,7 @@ def get_pretrained_state_dict(
     if "torch_dtype" in kwargs:
         dtype = kwargs["torch_dtype"]
         del kwargs["torch_dtype"]
-    official_model_name = get_official_model_name(official_model_name)
-    if official_model_name.startswith(NEED_REMOTE_CODE_MODELS) and not kwargs.get(
+    if official_model_name is not None and official_model_name.startswith(NEED_REMOTE_CODE_MODELS) and not kwargs.get(
         "trust_remote_code", False
     ):
         logging.warning(
@@ -1404,9 +1403,7 @@ def get_pretrained_state_dict(
         )
         kwargs["trust_remote_code"] = True
     if (
-        official_model_name.startswith("NeelNanda")
-        or official_model_name.startswith("ArthurConmy")
-        or official_model_name.startswith("Baidicoot")
+        official_name is not None and official_model_name.startswith(("NeelNanda", "ArthurConmy", "Baidicoot"))
     ):
         api = HfApi()
         repo_files = api.list_repo_files(