Instructions to use hf-tiny-model-private/tiny-random-DistilBertModel 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-DistilBertModel 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-DistilBertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DistilBertModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-DistilBertModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c624c671f9dec896df671d592221fe909a16af1b94a02f2a3289604994fbf78
- Size of remote file:
- 447 kB
- SHA256:
- 3d4ec6f559a144bf7b637124e87652d0a225cd20806bf07347a5f136cb0a67d3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.