Instructions to use hf-internal-testing/tiny-random-GPT2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-GPT2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-GPT2Model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-GPT2Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 461350ed7c51a13d1fe590cb97d305d3c0358c3947eebfe4f9aa37875094fb4f
- Size of remote file:
- 1.65 MB
- SHA256:
- 521bd506cf35a558b80e89a8efe998d9ef1d1552ee8ae090b63b29b6cf3c6072
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.