diff --git a/ai_edge_torch/generative/test/loader_test.py b/ai_edge_torch/generative/test/loader_test.py
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+++ b/ai_edge_torch/generative/test/loader_test.py
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+# Copyright 2024 The AI Edge Torch Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+# Testing weight loader utilities.
+
+import os
+import tempfile
+import unittest
+
+import safetensors.torch
+import torch
+
+from ai_edge_torch.generative.examples.tiny_llama import tiny_llama
+from ai_edge_torch.generative.utilities import loader as loading_utils
+
+
+class TestLoader(unittest.TestCase):
+  """Unit tests that check weight loader."""
+
+  def test_load_safetensors(self):
+    with tempfile.TemporaryDirectory() as temp_dir:
+      file_path = os.path.join(temp_dir, "test.safetensors")
+      test_data = {"weight": torch.randn(20, 10), "bias": torch.randn(20)}
+      safetensors.torch.save_file(test_data, file_path)
+
+      loaded_tensors = loading_utils.load_safetensors(file_path)
+      self.assertIn("weight", loaded_tensors)
+      self.assertIn("bias", loaded_tensors)
+
+  def test_load_statedict(self):
+    with tempfile.TemporaryDirectory() as temp_dir:
+      file_path = os.path.join(temp_dir, "test.pt")
+      model = torch.nn.Linear(10, 5)
+      state_dict = model.state_dict()
+      torch.save(state_dict, file_path)
+
+      loaded_tensors = loading_utils.load_pytorch_statedict(file_path)
+      self.assertIn("weight", loaded_tensors)
+      self.assertIn("bias", loaded_tensors)
+
+  def test_model_loader(self):
+    with tempfile.TemporaryDirectory() as temp_dir:
+      file_path = os.path.join(temp_dir, "test.safetensors")
+      test_weights = {
+          "lm_head.weight": torch.randn((32000, 2048)),
+          "model.embed_tokens.weight": torch.randn((32000, 2048)),
+          "model.layers.0.input_layernorm.weight": torch.randn((2048,)),
+          "model.layers.0.mlp.down_proj.weight": torch.randn((2048, 5632)),
+          "model.layers.0.mlp.gate_proj.weight": torch.randn((5632, 2048)),
+          "model.layers.0.mlp.up_proj.weight": torch.randn((5632, 2048)),
+          "model.layers.0.post_attention_layernorm.weight": torch.randn((2048,)),
+          "model.layers.0.self_attn.k_proj.weight": torch.randn((256, 2048)),
+          "model.layers.0.self_attn.o_proj.weight": torch.randn((2048, 2048)),
+          "model.layers.0.self_attn.q_proj.weight": torch.randn((2048, 2048)),
+          "model.layers.0.self_attn.v_proj.weight": torch.randn((256, 2048)),
+          "model.norm.weight": torch.randn((2048,)),
+      }
+      safetensors.torch.save_file(test_weights, file_path)
+      cfg = tiny_llama.get_model_config()
+      cfg.num_layers = 1
+      model = tiny_llama.TinyLLamma(cfg)
+
+      loader = loading_utils.ModelLoader(file_path, tiny_llama.TENSOR_NAMES)
+      # if returns successfully, it means all the tensors were initiallized.
+      loader.load(model, strict=True)
+
+
+if __name__ == "__main__":
+  unittest.main()