diff --git a/example/example_token_embeddings.py b/example/example_token_embeddings.py
index 0b80448..4539a27 100644
--- a/example/example_token_embeddings.py
+++ b/example/example_token_embeddings.py
@@ -10,7 +10,7 @@
 import torch
 import matplotlib.pyplot as plt
 from sklearn.decomposition import PCA
-from simple_llm.embedding.token_embedings import TokenEmbeddings
+from simple_llm.embedding.token_embeddings import TokenEmbeddings
 
 def basic_example():
     """Базовый пример использования TokenEmbeddings"""
diff --git a/tests/test_token_embeddings.py b/tests/test_token_embeddings.py
new file mode 100644
index 0000000..e28b747
--- /dev/null
+++ b/tests/test_token_embeddings.py
@@ -0,0 +1,47 @@
+import torch
+import pytest
+from simple_llm.embedding.token_embedings import TokenEmbeddings
+
+class TestTokenEmbeddings:
+    """Unit tests for TokenEmbeddings class"""
+    
+    @pytest.fixture
+    def embedding_layer(self):
+        return TokenEmbeddings(vocab_size=100, emb_size=32)
+    
+    def test_initialization(self, embedding_layer):
+        """Test layer initialization"""
+        assert isinstance(embedding_layer, torch.nn.Module)
+        assert embedding_layer._embedding.num_embeddings == 100
+        assert embedding_layer._embedding.embedding_dim == 32
+        
+    def test_forward_shape(self, embedding_layer):
+        """Test output shape of forward pass"""
+        test_input = torch.tensor([
+            [1, 2, 3],
+            [4, 5, 6]
+        ])
+        output = embedding_layer(test_input)
+        assert output.shape == (2, 3, 32)  # batch_size=2, seq_len=3, emb_size=32
+        
+    def test_embedding_values(self, embedding_layer):
+        """Test that embeddings are trainable"""
+        input_tensor = torch.tensor([[1]])
+        before = embedding_layer(input_tensor).clone()
+        
+        # Simulate training step
+        optimizer = torch.optim.SGD(embedding_layer.parameters(), lr=0.1)
+        loss = embedding_layer(input_tensor).sum()
+        loss.backward()
+        optimizer.step()
+        
+        after = embedding_layer(input_tensor)
+        assert not torch.allclose(before, after), "Embeddings should change after training"
+        
+    def test_out_of_vocab(self, embedding_layer):
+        """Test handling of out-of-vocabulary indices"""
+        with pytest.raises(IndexError):
+            embedding_layer(torch.tensor([[100]]))  # vocab_size=100
+            
+if __name__ == "__main__":
+    pytest.main(["-v", __file__])