Instructions to use hf-internal-testing/tiny-random-M2M100Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-M2M100Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-M2M100Model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-M2M100Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-M2M100Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da3e8efc745f9306803827307f8ee639726086fb361efac91ee660c408828db2
- Size of remote file:
- 8.24 MB
- SHA256:
- c955dd2cc49619cf195cd99534d9f4fbf416c3f3867eeb3d879f8d80a8359432
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.