Fix model loading issues and forecast_naive slicing bug in TimesFM 2.5

- Allow model wrapper constructors (__init__) to accept and ignore extra keyword arguments (e.g. proxies) passed by huggingface_hub during from_pretrained.
- Implement load_checkpoint for TimesFM_2p5_200M_torch and TimesFM_2p5_200M_flax to restore weights from local paths.
- Fix slicing bug in PyTorch's forecast_naive to correctly slice the time/horizon dimension ([:, :horizon, :]) instead of quantiles.
- Add unit tests in tests/test_model_loading.py covering local checkpoint loading, hub compatibility, and prediction shape correctness.

src/timesfm/timesfm_2p5/timesfm_2p5_flax.py

@@ -447,6 +447,21 @@ class TimesFM_2p5_200M_flax(timesfm_2p5_base.TimesFM_2p5):
 
   model: nnx.Module = TimesFM_2p5_200M_flax_module()
 
+  def __init__(self, **kwargs):
+    self.model = TimesFM_2p5_200M_flax_module()
+
+  def load_checkpoint(self, path: str):
+    """Loads a TimesFM model from a checkpoint."""
+    if os.path.isdir(path):
+      model_file_path = path
+    else:
+      model_file_path = os.path.dirname(path)
+
+    checkpointer = ocp.StandardCheckpointer()
+    graph, state = nnx.split(self.model)
+    state = checkpointer.restore(model_file_path, state)
+    self.model = nnx.merge(graph, state)
+
   @classmethod
   def from_pretrained(
     cls,
@@ -485,10 +500,7 @@ class TimesFM_2p5_200M_flax(timesfm_2p5_base.TimesFM_2p5):
       )
       logging.info("Loading checkpoint from: %s", model_file_path)
 
-    checkpointer = ocp.StandardCheckpointer()
-    graph, state = nnx.split(instance.model)
-    state = checkpointer.restore(model_file_path, state)
-    instance.model = nnx.merge(graph, state)
+    instance.load_checkpoint(model_file_path)
     return instance
 
   def compile(

src/timesfm/timesfm_2p5/timesfm_2p5_torch.py

@@ -257,7 +257,7 @@ class TimesFM_2p5_200M_torch_module(nn.Module):
       to_concat = [t_pf[:, -1, ...]]
       if t_ar is not None:
         to_concat.append(t_ar.reshape(1, -1, self.q))
-      torch_forecast = torch.cat(to_concat, dim=1)[..., :horizon]
+      torch_forecast = torch.cat(to_concat, dim=1)[:, :horizon, :]
       torch_forecast = torch_forecast.squeeze(0)
       outputs.append(torch_forecast.detach().cpu().numpy())
     return outputs
@@ -283,12 +283,26 @@ class TimesFM_2p5_200M_torch(
     self,
     torch_compile: bool = True,
     config: Optional[dict] = None,
+    **kwargs,
   ):
     self.model = TimesFM_2p5_200M_torch_module()
     self.torch_compile = torch_compile
     if config is not None:
       self._hub_mixin_config = config
 
+  def load_checkpoint(self, path: str, **kwargs):
+    """Loads a TimesFM model from a checkpoint directory or file."""
+    if os.path.isdir(path):
+      model_file_path = os.path.join(path, self.WEIGHTS_FILENAME)
+      if not os.path.exists(model_file_path):
+        raise FileNotFoundError(
+          f"{self.WEIGHTS_FILENAME} not found in directory {path}"
+        )
+    else:
+      model_file_path = path
+
+    self.model.load_checkpoint(model_file_path, **kwargs)
+
   @classmethod
   def _from_pretrained(
     cls,
@@ -333,7 +347,7 @@ class TimesFM_2p5_200M_torch(
 
     logging.info("Loading checkpoint from: %s", model_file_path)
     # Load the weights into the model.
-    instance.model.load_checkpoint(
+    instance.load_checkpoint(
       model_file_path, torch_compile=instance.torch_compile
     )
     return instance

tests/test_model_loading.py

@@ -0,0 +1,68 @@
+# Copyright 2025 Google LLC
+#
+# 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.
+
+"""Tests for loading TimesFM 2.5 models."""
+
+import os
+import tempfile
+
+from timesfm.timesfm_2p5.timesfm_2p5_torch import TimesFM_2p5_200M_torch
+from timesfm.timesfm_2p5.timesfm_2p5_flax import TimesFM_2p5_200M_flax
+
+
+class TestModelLoading:
+  """Tests to verify model instantiation, loading, and compatibility."""
+
+  def test_torch_load_checkpoint_and_from_pretrained_local(self):
+    """Verifies that PyTorch load_checkpoint and from_pretrained work locally."""
+    # 1. Instantiate the model wrapper with compilation disabled
+    tfm = TimesFM_2p5_200M_torch(torch_compile=False)
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+      # 2. Save the model's randomly-initialized weights
+      tfm._save_pretrained(tmpdir)
+
+      # Verify weights file is written
+      weights_path = os.path.join(tmpdir, "model.safetensors")
+      assert os.path.exists(weights_path)
+
+      # 3. Verify that load_checkpoint works from the temp directory path
+      tfm2 = TimesFM_2p5_200M_torch(torch_compile=False)
+      tfm2.load_checkpoint(tmpdir, torch_compile=False)
+
+      # 4. Verify that from_pretrained works with a local directory path
+      # and accepts/ignores extra kwargs (like proxies) without raising TypeError
+      tfm3 = TimesFM_2p5_200M_torch.from_pretrained(
+          tmpdir,
+          torch_compile=False,
+          proxies={"http": "http://dummy.proxy"},
+          custom_kwarg="dummy_value",
+      )
+      assert tfm3 is not None
+      assert not tfm3.torch_compile
+
+      # 5. Run a simple prediction step to verify the loaded model performs forward pass
+      import numpy as np
+      inputs = [np.random.randn(32)]
+      forecasts = tfm3.model.forecast_naive(horizon=10, inputs=inputs)
+      assert len(forecasts) == 1
+      assert forecasts[0].shape == (10, 10)
+
+  def test_flax_model_init_kwargs(self):
+    """Verifies that Flax model wrapper constructor accepts arbitrary kwargs."""
+    tfm = TimesFM_2p5_200M_flax(
+        proxies={"http": "http://dummy.proxy"},
+        custom_kwarg="dummy_value",
+    )
+    assert tfm is not None