Instructions to use hf-internal-testing/tiny-random-PatchTSTModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PatchTSTModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-PatchTSTModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-PatchTSTModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-PatchTSTModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ac83fbef7d915fea752ead778d0ed117326b5c0a30191bb7ec57ba151ce43eff
- Size of remote file:
- 749 kB
- SHA256:
- 25a52c0cbeda310422076ca5c6a1dc2c175a044c6f2a672877f0c8f8a75ddb77
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.