Instructions to use hf-tiny-model-private/tiny-random-OpenAIGPTModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-OpenAIGPTModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-OpenAIGPTModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-OpenAIGPTModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-OpenAIGPTModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d7202ee3c8ac9b47bcb82c4dd7b51aff9dcb5cbc8d7b4feebced4dc0ddee629
- Size of remote file:
- 5.75 MB
- SHA256:
- 92271b0d447f2fdd6750abdb23979b173331f26af3f0b77e109f768883fff21c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.