timesfm-2.0-500m-jax
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timesfm-2.0-500m-pytorch
GoogleTimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. * Paper: A decoder-only foundation model for time-series forecasting, ICML 2024. * Google Research blog * GitHub repo - It performs univariate time series forecasting for context lengths up to 2048 time points and any horizon lengths, with an optional frequency indicator. Note that it can go even beyond 2048 context even though it was trained with that as the maximum context. - It focuses on point forecasts. We experimentally offer 10 quantile heads but they have not been calibrated after pretraining. - It ideally requires the context to be contiguous (i.e. no "holes"), and the context and the horizon to be of the same frequency. In case there are nans we fill in the missing values with linear interpolation before calling the model.
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View details ↗timesfm-2.5-200m-flax
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View details ↗timesfm-2.5-200m-pytorch
GoogleTimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. * October 2, 2025: We changed the structure of the model to fuse QKV matrices into one for speed optimization. Please reinstall the latest version of the timesfm package to reflect these changes. Results should be unchanged. **Resources and Technical Documentation**: * Paper: A decoder-only foundation model for time-series forecasting, ICML 2024. * Google Research blog * GitHub repo
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View details ↗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 * Paper: A decoder-only foundation model for time-series forecasting * Transformers docs: TimesFM 2.5 This model is converted from the official TimesFM 2.5 PyTorch checkpoint and integrated into `transformers` as `TimesFm2_5ModelForPrediction`.
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View details ↗timesfm-3.0-pytorch
GoogleTimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. This repository contains the official PyTorch weights and configurations for **TimesFM 3.0**. This model is released under the **TimesFM Non-Commercial License v1.0**.
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View details ↗tipsv1-b14
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View details ↗tipsv1-g14
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View details ↗tipsv1-g14-lowres
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View details ↗tipsv1-s14
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View details ↗tipsv1-so400m14
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View details ↗tipsv2-b14
GoogleTIPSv2 (Text-Image Pre-training with Spatial awareness) is a family of contrastive vision-language models that produce spatially rich image features aligned with text embeddings. This is the Base variant with 86M vision params and 110M text params. Try the code snippets below or check out the GitHub repo for more use cases and visualizations, including zero-shot segmentation.
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View details ↗tipsv2-b14-dpt
GoogleDPT (Dense Prediction Transformer) heads for depth estimation, surface normal prediction, and semantic segmentation on top of the frozen TIPSv2 B/14 backbone. The backbone is loaded automatically. The depth and normals heads are trained on the NYU Depth V2 dataset and segmentation is trained on the ADE20K dataset (150 classes).
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View details ↗tipsv2-g14
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View details ↗tipsv2-g14-dpt
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View details ↗tipsv2-l14
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View details ↗tipsv2-l14-dpt
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View details ↗tipsv2-so400m14
GoogleTIPSv2 (Text-Image Pre-training with Spatial awareness) is a family of contrastive vision-language models that produce spatially rich image features aligned with text embeddings. This is the SO400m variant with 412M vision params and 448M text params. Try the code snippets below or check out the GitHub repo for more use cases and visualizations, including zero-shot segmentation. Images should be tensors in `[0, 1]` range (just `ToTensor()`, no ImageNet normalization). - **Architecture**: ViT vision encoder (27 layers) + Transformer text encoder (27 layers) - **Image preprocessing**: resize to any resolution, convert to `[0, 1]` (no ImageNet normalization) - **Text preprocessing**: SentencePiece tokenizer, lowercased, max 64 tokens - **Patch size**: 14x14 pixels
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View details ↗tipsv2-so400m14-dpt
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View details ↗translategemma-12b-it
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View details ↗translategemma-27b-it
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View details ↗translategemma-4b-it
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View details ↗txgemma-27b-chat
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