Instructions to use hf-tiny-model-private/tiny-random-BloomModel 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-BloomModel 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-BloomModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-BloomModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-BloomModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 36967b13b0a3c54daf6da8dad4fba1da0d53ea12978d613b9e2d7863a7eab306
- Size of remote file:
- 405 kB
- SHA256:
- 83b54a5a45f78bee72480092a940ae1e337a6e64fd588f54b0a1c9ca6eca44c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.