Instructions to use hf-internal-testing/tiny-random-SeamlessM4Tv2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SeamlessM4Tv2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-SeamlessM4Tv2Model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SeamlessM4Tv2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-SeamlessM4Tv2Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df48a838f4a121deed8006fd8e830480368a7003ce3e26cd7b5620cd952661a8
- Size of remote file:
- 338 kB
- SHA256:
- 1081748e19a4bd603cd34fbddcf5086f6df6e0cda8411e59b5cab2780494a435
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.