Instructions to use google/timesfm-2.5-200m-transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/timesfm-2.5-200m-transformers with Transformers:
# Load model directly from transformers import AutoTokenizer, TimesFm2_5ModelForPrediction tokenizer = AutoTokenizer.from_pretrained("google/timesfm-2.5-200m-transformers") model = TimesFm2_5ModelForPrediction.from_pretrained("google/timesfm-2.5-200m-transformers", device_map="auto") - TimesFM
How to use google/timesfm-2.5-200m-transformers with TimesFM:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59545b5f6a44258dc78d7be613a602db534495065ffe3eed2b68f12b7781c08e
- Size of remote file:
- 925 MB
- SHA256:
- b53f6d52114e2ad786890f3c4637ce05f580b7800d6e24401f88b398b76035ef
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