feat: add LoRA/DoRA adapter layers for TimesFM 2.5 (PyTorch)

Implement production-grade PEFT adapters targeting the 2.5 architecture:

- LoRALinear: low-rank A/B decomposition with scaling (alpha/rank)
- DoRALinear: weight-decomposed LoRA (magnitude + direction)
- inject_adapters(): freezes base weights, wraps target nn.Linear modules
  - Supports fused QKV (qkv_proj), attention output, and FFN layers
  - num_adapter_layers controls how many top layers get adapters (0=all)
  - target_modules selects 'all', 'attention', or 'ffn'
- merge_adapters(): folds adapter deltas back into base nn.Linear
- save/load_adapter_weights(): safetensors adapter-only checkpoints
- PEFTConfig dataclass with all hyperparameters

References:
  LoRA — https://arxiv.org/abs/2106.09685
  DoRA — https://arxiv.org/abs/2402.09353

peft/__init__.py

@@ -0,0 +1,41 @@
+# 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.
+
+"""PEFT (LoRA/DoRA) fine-tuning pipeline for TimesFM 2.5."""
+
+from .adapters import (
+    DoRALinear,
+    LoRALinear,
+    get_adapter_params,
+    inject_adapters,
+    load_adapter_weights,
+    merge_adapters,
+    save_adapter_weights,
+)
+from .config import PEFTConfig
+from .data import TimeSeriesDataset
+from .trainer import PEFTTrainer
+
+__all__ = [
+    "PEFTConfig",
+    "PEFTTrainer",
+    "TimeSeriesDataset",
+    "LoRALinear",
+    "DoRALinear",
+    "inject_adapters",
+    "merge_adapters",
+    "save_adapter_weights",
+    "load_adapter_weights",
+    "get_adapter_params",
+]