Instructions to use hf-internal-testing/tiny-random-ErnieMModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ErnieMModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-ErnieMModel", device_map="auto")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-ErnieMModel", dtype="auto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a1b0f586dc1728dab5829f2cefe67e0a0b76abed5e85224a01ef6b16565678f7
- Size of remote file:
- 32.2 MB
- SHA256:
- ce669c3c241c6ec06f5a213f72ae33981467f2c2de530cc923bbd8f62178eaac
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.