Instructions to use hf-internal-testing/tiny-random-BloomModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BloomModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-BloomModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BloomModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-BloomModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bdbdfe9deec99192cb3b49c2478cb208126afc9ff3e0fa11ef7d84438f08385d
- Size of remote file:
- 392 kB
- SHA256:
- 0cc2addc2f6a3413ee8294ba1b095b3030f3b210249aca43f46c79eec63351b0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.