diff --git a/mlx_video/models/ltx_2/__init__.py b/mlx_video/models/ltx_2/__init__.py
index f382326..dd2b1e0 100644
--- a/mlx_video/models/ltx_2/__init__.py
+++ b/mlx_video/models/ltx_2/__init__.py
@@ -4,4 +4,4 @@ from mlx_video.models.ltx_2.config import (
     LTXModelType,
     TransformerConfig,
 )
-from mlx_video.models.ltx_2.ltx import LTXModel, X0Model
+from mlx_video.models.ltx_2.ltx_2 import LTXModel, X0Model
diff --git a/mlx_video/models/ltx_2/generate.py b/mlx_video/models/ltx_2/generate.py
index 6c3fc72..c6c592d 100644
--- a/mlx_video/models/ltx_2/generate.py
+++ b/mlx_video/models/ltx_2/generate.py
@@ -33,7 +33,7 @@ from mlx_video.models.ltx_2.conditioning import (
     apply_conditioning,
 )
 from mlx_video.models.ltx_2.conditioning.latent import LatentState, apply_denoise_mask
-from mlx_video.models.ltx_2.ltx import LTXModel
+from mlx_video.models.ltx_2.ltx_2 import LTXModel
 from mlx_video.models.ltx_2.transformer import Modality
 from mlx_video.models.ltx_2.upsampler import load_upsampler, upsample_latents
 from mlx_video.models.ltx_2.video_vae import VideoEncoder
diff --git a/mlx_video/models/ltx_2/ltx.py b/mlx_video/models/ltx_2/ltx_2.py
similarity index 100%
rename from mlx_video/models/ltx_2/ltx.py
rename to mlx_video/models/ltx_2/ltx_2.py
diff --git a/mlx_video/models/wan2/__init__.py b/mlx_video/models/wan2/__init__.py
index b9c08ac..90390e9 100644
--- a/mlx_video/models/wan2/__init__.py
+++ b/mlx_video/models/wan2/__init__.py
@@ -1,2 +1,2 @@
 from mlx_video.models.wan2.config import WanModelConfig
-from mlx_video.models.wan2.model import WanModel
+from mlx_video.models.wan2.wan2 import WanModel
diff --git a/mlx_video/models/wan2/convert.py b/mlx_video/models/wan2/convert.py
index 8ae510f..ba2b79a 100644
--- a/mlx_video/models/wan2/convert.py
+++ b/mlx_video/models/wan2/convert.py
@@ -594,7 +594,7 @@ def _quantize_saved_model(
 
     import mlx.nn as nn
 
-    from mlx_video.models.wan2.model import WanModel
+    from mlx_video.models.wan2.wan2 import WanModel
 
     if source_dir is None:
         source_dir = output_dir
diff --git a/mlx_video/models/wan2/utils.py b/mlx_video/models/wan2/utils.py
index 6c9be4f..45964fe 100644
--- a/mlx_video/models/wan2/utils.py
+++ b/mlx_video/models/wan2/utils.py
@@ -21,7 +21,7 @@ def load_wan_model(
                       If provided, creates QuantizedLinear stubs before loading.
         loras: Optional list of (lora_path, strength) tuples to apply.
     """
-    from mlx_video.models.wan2.model import WanModel
+    from mlx_video.models.wan2.wan2 import WanModel
 
     model = WanModel(config)
 
diff --git a/mlx_video/models/wan2/model.py b/mlx_video/models/wan2/wan2.py
similarity index 100%
rename from mlx_video/models/wan2/model.py
rename to mlx_video/models/wan2/wan2.py
diff --git a/tests/test_wan_generate.py b/tests/test_wan_generate.py
index f4d1682..e586cce 100644
--- a/tests/test_wan_generate.py
+++ b/tests/test_wan_generate.py
@@ -14,7 +14,7 @@ class TestEndToEnd:
 
     def test_tiny_model_denoise_step(self):
         """Simulate one denoising step with tiny model."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
         from mlx_video.models.wan2.scheduler import FlowMatchEulerScheduler
 
         mx.random.seed(42)
@@ -43,7 +43,7 @@ class TestEndToEnd:
 
     def test_tiny_model_full_loop(self):
         """Run a complete (tiny) diffusion loop."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
         from mlx_video.models.wan2.scheduler import FlowMatchEulerScheduler
 
         mx.random.seed(123)
@@ -201,7 +201,7 @@ class TestDimensionAlignment:
 
     def test_patchify_valid_after_alignment(self):
         """After alignment, patchify should succeed without reshape errors."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_config()
         model = WanModel(config)
diff --git a/tests/test_wan_i2v.py b/tests/test_wan_i2v.py
index b2a4bab..7c5e0cd 100644
--- a/tests/test_wan_i2v.py
+++ b/tests/test_wan_i2v.py
@@ -66,7 +66,7 @@ class TestModelYParameter:
 
     def test_forward_without_y(self):
         """Standard T2V forward pass (no y) still works."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_config()
         model = WanModel(config)
@@ -85,7 +85,7 @@ class TestModelYParameter:
 
     def test_forward_with_y(self):
         """I2V forward pass with y channel concatenation."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_i2v_config()
         model = WanModel(config)
@@ -108,7 +108,7 @@ class TestModelYParameter:
 
     def test_y_none_is_noop(self):
         """Passing y=None should be identical to not passing y."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_config()
         model = WanModel(config)
@@ -129,7 +129,7 @@ class TestModelYParameter:
 
     def test_batched_cfg_with_y(self):
         """Batched CFG (B=2) with y should work."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_i2v_config()
         model = WanModel(config)
@@ -307,7 +307,7 @@ class TestI2VEndToEndPipeline:
 
     def test_full_i2v_pipeline(self):
         """End-to-end I2V: synthetic image → VAE encode → build y → denoise → VAE decode."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
         from mlx_video.models.wan2.scheduler import FlowMatchEulerScheduler
         from mlx_video.models.wan2.vae import WanVAE
 
@@ -410,7 +410,7 @@ class TestDualModelSwitching:
 
     def test_model_selection_by_timestep(self):
         """Verify high_noise model used for timesteps >= boundary, low_noise otherwise."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
         from mlx_video.models.wan2.scheduler import FlowMatchEulerScheduler
 
         mx.random.seed(1)
@@ -485,7 +485,7 @@ class TestDualModelSwitching:
 
     def test_guide_scale_tuple_applied_per_model(self):
         """Verify (low_gs, high_gs) tuple applies different scales per model."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
         from mlx_video.models.wan2.scheduler import FlowMatchEulerScheduler
 
         mx.random.seed(2)
@@ -545,7 +545,7 @@ class TestDualModelSwitching:
 
     def test_single_model_fallback_with_tuple_guide_scale(self):
         """When dual_model=False, guide_scale tuple should use first element."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
         from mlx_video.models.wan2.scheduler import FlowMatchEulerScheduler
 
         mx.random.seed(3)
diff --git a/tests/test_wan_model.py b/tests/test_wan_model.py
index 650e0e5..e415052 100644
--- a/tests/test_wan_model.py
+++ b/tests/test_wan_model.py
@@ -12,7 +12,7 @@ from wan_test_helpers import _make_tiny_config
 
 class TestSinusoidalEmbedding:
     def test_output_shape(self):
-        from mlx_video.models.wan2.model import sinusoidal_embedding_1d
+        from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
 
         pos = mx.arange(10).astype(mx.float32)
         emb = sinusoidal_embedding_1d(256, pos)
@@ -21,7 +21,7 @@ class TestSinusoidalEmbedding:
 
     def test_position_zero(self):
         """Position 0 should have cos=1 for all dims and sin=0."""
-        from mlx_video.models.wan2.model import sinusoidal_embedding_1d
+        from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
 
         pos = mx.array([0.0])
         emb = sinusoidal_embedding_1d(64, pos)
@@ -33,7 +33,7 @@ class TestSinusoidalEmbedding:
         np.testing.assert_allclose(emb_np[32:], 0.0, atol=1e-5)
 
     def test_different_positions_differ(self):
-        from mlx_video.models.wan2.model import sinusoidal_embedding_1d
+        from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
 
         pos = mx.array([0.0, 100.0, 999.0])
         emb = sinusoidal_embedding_1d(128, pos)
@@ -50,7 +50,7 @@ class TestSinusoidalEmbedding:
 
 class TestHead:
     def test_output_shape(self):
-        from mlx_video.models.wan2.model import Head
+        from mlx_video.models.wan2.wan2 import Head
 
         head = Head(dim=64, out_dim=16, patch_size=(1, 2, 2))
         B, L = 1, 24
@@ -62,7 +62,7 @@ class TestHead:
         assert out.shape == (B, L, expected_proj_dim)
 
     def test_modulation_shape(self):
-        from mlx_video.models.wan2.model import Head
+        from mlx_video.models.wan2.wan2 import Head
 
         head = Head(dim=64, out_dim=16, patch_size=(1, 2, 2))
         assert head.modulation.shape == (1, 2, 64)
@@ -78,7 +78,7 @@ class TestWanModel:
         mx.random.seed(42)
 
     def test_instantiation(self):
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_config()
         model = WanModel(config)
@@ -86,7 +86,7 @@ class TestWanModel:
         assert num_params > 0
 
     def test_patchify_shape(self):
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_config()
         model = WanModel(config)
@@ -99,7 +99,7 @@ class TestWanModel:
         assert patches.shape == (1, 1 * 2 * 2, config.dim)
 
     def test_patchify_various_sizes(self):
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_config()
         model = WanModel(config)
@@ -115,7 +115,7 @@ class TestWanModel:
 
     def test_unpatchify_inverse(self):
         """Patchify then unpatchify should reconstruct original spatial dims."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_config()
         model = WanModel(config)
@@ -131,7 +131,7 @@ class TestWanModel:
         assert out[0].shape == (config.out_dim, F, H, W)
 
     def test_forward_pass(self):
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_config()
         model = WanModel(config)
@@ -149,7 +149,7 @@ class TestWanModel:
         assert out[0].shape == (C, F, H, W)
 
     def test_forward_batch(self):
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_config()
         model = WanModel(config)
@@ -171,7 +171,7 @@ class TestWanModel:
             assert o.shape == (C, F, H, W)
 
     def test_output_is_float32(self):
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_config()
         model = WanModel(config)
@@ -234,7 +234,7 @@ class TestWan21Model:
 
     def test_wan21_tiny_model_forward(self):
         """Forward pass with Wan2.1 tiny config."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = self._make_tiny_wan21_config()
         model = WanModel(config)
@@ -252,7 +252,7 @@ class TestWan21Model:
 
     def test_wan21_1_3b_tiny_model_forward(self):
         """Forward pass with Wan2.1 1.3B tiny config."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = self._make_tiny_wan21_1_3b_config()
         model = WanModel(config)
@@ -270,7 +270,7 @@ class TestWan21Model:
 
     def test_wan21_single_model_loop(self):
         """Full diffusion loop with single model (Wan2.1 style)."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
         from mlx_video.models.wan2.scheduler import FlowMatchEulerScheduler
 
         config = self._make_tiny_wan21_config()
@@ -333,21 +333,21 @@ class TestPerTokenTimestep:
     """Tests for per-token sinusoidal embedding."""
 
     def test_1d_unchanged(self):
-        from mlx_video.models.wan2.model import sinusoidal_embedding_1d
+        from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
 
         pos = mx.array([0.0, 100.0, 500.0])
         emb = sinusoidal_embedding_1d(256, pos)
         assert emb.shape == (3, 256)
 
     def test_2d_per_token(self):
-        from mlx_video.models.wan2.model import sinusoidal_embedding_1d
+        from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
 
         pos = mx.array([[0.0, 100.0, 100.0], [50.0, 50.0, 50.0]])
         emb = sinusoidal_embedding_1d(256, pos)
         assert emb.shape == (2, 3, 256)
 
     def test_consistency(self):
-        from mlx_video.models.wan2.model import sinusoidal_embedding_1d
+        from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
 
         pos_1d = mx.array([0.0, 100.0])
         emb_1d = sinusoidal_embedding_1d(256, pos_1d)
diff --git a/tests/test_wan_quantization.py b/tests/test_wan_quantization.py
index 1eb9622..14fe3ca 100644
--- a/tests/test_wan_quantization.py
+++ b/tests/test_wan_quantization.py
@@ -91,7 +91,7 @@ class TestQuantizeRoundTrip:
     def _quantize_and_save(self, config, tmp_path, bits=4, group_size=64):
         """Helper: create model, quantize, save to tmp_path."""
         from mlx_video.models.wan2.convert import _quantize_predicate
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         model = WanModel(config)
         nn.quantize(
@@ -164,7 +164,7 @@ class TestQuantizeRoundTrip:
 
     def test_loading_without_quantization_flag(self, tmp_path):
         """Loading a non-quantized model should have standard Linear layers."""
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_config()
         model = WanModel(config)
@@ -188,7 +188,7 @@ class TestQuantizeRoundTrip:
 class TestQuantizedInference:
     def _make_quantized_model(self, config, bits=4):
         from mlx_video.models.wan2.convert import _quantize_predicate
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         model = WanModel(config)
         nn.quantize(
@@ -239,7 +239,7 @@ class TestQuantizedInference:
     def test_quantized_output_differs_from_unquantized(self):
         """Sanity check: quantization should change the weights."""
         from mlx_video.models.wan2.convert import _quantize_predicate
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_config()
         mx.random.seed(42)
@@ -272,7 +272,7 @@ class TestQuantizationConfig:
     def test_config_metadata_written(self, tmp_path):
         """Verify _quantize_saved_model writes quantization metadata to config.json."""
         from mlx_video.models.wan2.convert import _quantize_saved_model
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_config()
         model = WanModel(config)
@@ -296,7 +296,7 @@ class TestQuantizationConfig:
 
     def test_config_metadata_8bit(self, tmp_path):
         from mlx_video.models.wan2.convert import _quantize_saved_model
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_config()
         model = WanModel(config)
@@ -317,7 +317,7 @@ class TestQuantizationConfig:
     def test_dual_model_quantization(self, tmp_path):
         """Verify dual-model quantization writes both model files."""
         from mlx_video.models.wan2.convert import _quantize_saved_model
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = _make_tiny_config()
 
diff --git a/tests/test_wan_rope_freqs.py b/tests/test_wan_rope_freqs.py
index 5da2a5f..93324a5 100644
--- a/tests/test_wan_rope_freqs.py
+++ b/tests/test_wan_rope_freqs.py
@@ -28,7 +28,7 @@ class TestRoPEFrequencyConstruction:
     def _get_model_freqs(self, dim=64, num_heads=4):
         """Instantiate a tiny WanModel and return its .freqs tensor."""
         from mlx_video.models.wan2.config import WanModelConfig
-        from mlx_video.models.wan2.model import WanModel
+        from mlx_video.models.wan2.wan2 import WanModel
 
         config = WanModelConfig()
         config.dim = dim
diff --git a/tests/test_wan_transformer.py b/tests/test_wan_transformer.py
index 7d197c2..66df8c5 100644
--- a/tests/test_wan_transformer.py
+++ b/tests/test_wan_transformer.py
@@ -153,7 +153,7 @@ class TestFloat32Modulation:
 
     def test_head_modulation_float32(self):
         """Head modulation should be float32 even with bf16 e input."""
-        from mlx_video.models.wan2.model import Head
+        from mlx_video.models.wan2.wan2 import Head
 
         head = Head(self.dim, 4, (1, 2, 2))
         x = mx.random.normal((1, 8, self.dim))
@@ -164,7 +164,7 @@ class TestFloat32Modulation:
 
     def test_model_time_embedding_float32(self):
         """sinusoidal_embedding_1d output must be float32."""
-        from mlx_video.models.wan2.model import sinusoidal_embedding_1d
+        from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
 
         t = mx.array([500.0])
         emb = sinusoidal_embedding_1d(256, t)
@@ -173,7 +173,7 @@ class TestFloat32Modulation:
 
     def test_model_per_token_time_embedding_float32(self):
         """Per-token time embeddings (I2V) should also be float32."""
-        from mlx_video.models.wan2.model import sinusoidal_embedding_1d
+        from mlx_video.models.wan2.wan2 import sinusoidal_embedding_1d
 
         t = mx.array([[0.0, 100.0, 200.0, 300.0]])  # [B=1, L=4]
         emb = sinusoidal_embedding_1d(256, t)