diff --git a/model2vec/distill/distillation.py b/model2vec/distill/distillation.py
index 8df47b88..9703bba5 100644
--- a/model2vec/distill/distillation.py
+++ b/model2vec/distill/distillation.py
@@ -204,6 +204,7 @@ def distill(
     apply_zipf: bool = True,
     use_subword: bool = True,
     token_remove_pattern: str | None = r"\[unused\d+\]",
+    trust_remote_code: bool = False,
 ) -> StaticModel:
     """
     Distill a staticmodel from a sentence transformer.
@@ -223,11 +224,12 @@ def distill(
     :param apply_zipf: Whether to apply Zipf weighting to the embeddings.
     :param use_subword: Whether to keep subword tokens in the vocabulary. If this is False, you must pass a vocabulary, and the returned tokenizer will only detect full words.
     :param token_remove_pattern: If this is set to a string, we compile this into a regex. Any tokens that conform to this regex pattern will be removed from the vocabulary.
+    :param trust_remote_code: Whether to trust the remote code. If this is False, we will only load components coming from `transformers`. If this is True, we will load all components.
     :return: A StaticModel
 
     """
-    model: PreTrainedModel = AutoModel.from_pretrained(model_name)
-    tokenizer: PreTrainedTokenizerFast = AutoTokenizer.from_pretrained(model_name)
+    model: PreTrainedModel = AutoModel.from_pretrained(model_name, trust_remote_code=trust_remote_code)
+    tokenizer: PreTrainedTokenizerFast = AutoTokenizer.from_pretrained(model_name, trust_remote_code=trust_remote_code)
 
     return distill_from_model(
         model=model,