Update README.md (#1)

- Update README.md (3fe80c76086e8fd8c1a4ea63df2bdca94be7dd04)


Co-authored-by: Kashif Rasul <kashif@users.noreply.huggingface.co>

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: time-series-forecasting
+tags:
+- transformers
+- timesfm
+- timesfm_2p5
+- time-series-forecasting
+- arxiv:2310.10688
+---
+
+# TimesFM 2.5 (Transformers)
+
+TimesFM (Time Series Foundation Model) is a pretrained decoder-only model for time-series forecasting. This repository contains the **Transformers** port of the official TimesFM 2.5 PyTorch release.
+
+**Resources and Technical Documentation**:
+* Original model: [google/timesfm-2.5-200m-pytorch](https://huggingface.co/google/timesfm-2.5-200m-pytorch)
+* Transformers model: [google/timesfm-2.5-200m-transformers](https://huggingface.co/google/timesfm-2.5-200m-transformers)
+* Paper: [A decoder-only foundation model for time-series forecasting](https://huggingface.co/papers/2310.10688)
+* Transformers docs: [TimesFM 2.5](https://huggingface.co/docs/transformers/main/en/model_doc/timesfm_2p5)
+
+## Model description
+
+This model is converted from the official TimesFM 2.5 PyTorch checkpoint and integrated into `transformers` as `Timesfm2P5ModelForPrediction`.
+
+The converted checkpoint preserves the original architecture and forecasting behavior, including:
+* patch-based inputs for time-series contexts
+* decoder-only self-attention stack
+* point and quantile forecasts
+
+## Usage (Transformers)
+
+```python
+import torch
+from transformers import Timesfm2P5ModelForPrediction
+
+model = Timesfm2P5ModelForPrediction.from_pretrained("google/timesfm-2.5-200m-transformers", attn_implementation="sdpa")
+model = model.to(torch.float32).eval()
+
+past_values = [
+    torch.linspace(0, 1, 100),
+    torch.sin(torch.linspace(0, 20, 67)),
+]
+
+with torch.no_grad():
+    outputs = model(past_values=past_values, forecast_context_len=1024)
+
+print(outputs.mean_predictions.shape)
+print(outputs.full_predictions.shape)
+```
+
+## Conversion details
+
+This checkpoint was produced with:
+* script: `src/transformers/models/timesfm_2p5/convert_timesfm_2p5_original_to_hf.py`
+* source checkpoint: `google/timesfm-2.5-200m-pytorch`
+* conversion date (UTC): `2026-02-20`
+
+Weight conversion parity is verified by comparing converted-model forecasts against the official implementation outputs on deterministic inputs.
+
+## Citation
+
+```bibtex
+@inproceedings{das2024a,
+    title={A decoder-only foundation model for time-series forecasting},
+    author={Abhimanyu Das and Weihao Kong and Rajat Sen and Yichen Zhou},
+    booktitle={Forty-first International Conference on Machine Learning},
+    year={2024},
+    url={https://openreview.net/forum?id=jn2iTJas6h}
+}
+```