first commit

docs/source/en/model_doc/timesfm2_5.md

@@ -0,0 +1,78 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was published in HF papers on 2023-10-14 and contributed to Hugging Face Transformers on 2026-02-27.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
+
+# TimesFM 2.5
+
+## Overview
+
+TimesFM 2.5 (Time Series Foundation Model) is a pretrained time-series foundation model proposed in [A decoder-only foundation model for time-series forecasting](https://huggingface.co/papers/2310.10688) by Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou. It builds on the original TimesFM architecture with rotary attention, QK normalization, per-dimension attention scaling, and continuous quantile prediction.
+
+The abstract from the paper is the following:
+
+*Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset. Our model is based on pretraining a decoder style attention model with input patching, using a large time-series corpus comprising both real-world and synthetic datasets. Experiments on a diverse set of previously unseen forecasting datasets suggests that the model can yield accurate zero-shot forecasts across different domains, forecasting horizons and temporal granularities.*
+
+This model was contributed by [kashif](https://huggingface.co/kashif). The original code can be found [here](https://github.com/google-research/timesfm).
+
+You can find the checkpoint at [`google/timesfm-2.5-200m-transformers`](https://huggingface.co/google/timesfm-2.5-200m-transformers).
+
+## Usage example
+
+```python
+import numpy as np
+import torch
+
+from transformers import TimesFm2_5ModelForPrediction
+
+
+model = TimesFm2_5ModelForPrediction.from_pretrained(
+    "google/timesfm-2.5-200m-transformers",
+    device_map="auto",
+)
+
+forecast_input = [
+    np.sin(np.linspace(0, 20, 100)),
+    np.sin(np.linspace(0, 20, 200)),
+    np.sin(np.linspace(0, 20, 400)),
+]
+forecast_input_tensor = [torch.tensor(ts, dtype=torch.float32, device=model.device) for ts in forecast_input]
+
+with torch.no_grad():
+    outputs = model(past_values=forecast_input_tensor, return_dict=True)
+    point_forecast = outputs.mean_predictions
+    quantile_forecast = outputs.full_predictions
+```
+
+## TimesFm2_5Config
+
+[[autodoc]] TimesFm2_5Config
+
+## TimesFm2_5Model
+
+[[autodoc]] TimesFm2_5Model
+    - forward
+
+## TimesFm2_5ModelForPrediction
+
+[[autodoc]] TimesFm2_5ModelForPrediction
+    - forward