diff --git a/demo.ipynb b/demo.ipynb
index 27b085c..2c230f5 100644
--- a/demo.ipynb
+++ b/demo.ipynb
@@ -29,7 +29,13 @@
    "source": [
     "from tako_query_filter.filter import TakoQueryFilter\n",
     "\n",
-    "query_filter = TakoQueryFilter.load_from_hf(force_download=True)"
+    "query_filter = TakoQueryFilter.load_from_hf(force_download=True)\n",
+    "# Alternatively, you can load from local paths\n",
+    "# query_filter = TakoQueryFilter.load_from_local(\n",
+    "#     topic_model_path=\"local-scikit-model-path/topic_model.pkl\",\n",
+    "#     spacy_model_path=\"local-spacy-model-path\",\n",
+    "#     keywords_path=\"local-scikit-model-path/topic_model.pkl\"\n",
+    "# )\n"
    ]
   },
   {
diff --git a/pyproject.toml b/pyproject.toml
index fb28132..de52fa8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ description = "Combines models to predict which queries Tako's API should handle
 name = "tako-query-filter"
 packages = [{include = "tako_query_filter", from = "src"}]
 readme = "README.md"
-version = "0.1.2"
+version = "0.1.3"
 
 [tool.poetry.dependencies]
 huggingface-hub = "0.26.1"
diff --git a/src/tako_query_filter/filter.py b/src/tako_query_filter/filter.py
index e23a1b4..f0f45bc 100644
--- a/src/tako_query_filter/filter.py
+++ b/src/tako_query_filter/filter.py
@@ -57,6 +57,31 @@ def load_from_hf(
 
         return cls(topic_model, spacy_model, keywords)
 
+    @classmethod
+    def load_from_local(
+        cls,
+        topic_model_path: str,
+        spacy_model_path: str,
+        keywords_path: str,
+    ):
+        """Load TakoQueryFilter from local file paths.
+
+        Args:
+            topic_model_path: Path to the scikit-learn topic model pickle file
+            spacy_model_path: Path to the spacy model directory
+            keywords_path: Path to the whitelist keywords JSON file
+
+        Returns:
+            TakoQueryFilter: Initialized filter with models loaded from local paths
+        """
+        topic_model = joblib.load(topic_model_path)
+        spacy_model = spacy.load(spacy_model_path)
+
+        with open(keywords_path, "r") as f:
+            keywords = set(json.load(f))
+
+        return cls(topic_model, spacy_model, keywords)
+
     def create_embeddings(self, queries: Iterable[str]) -> np.ndarray:
         if not self.embeddings_model:
             from sentence_transformers import SentenceTransformer