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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ceb6bb65c2dcf9d017961f0d36f17d68153700cd4881ac68906a9abe503602eb
- Size of remote file:
- 338 kB
- SHA256:
- 220603b67ba9b083602984224d0456980f20ad8b67a1a7f4e53c6ce57b55c3e6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.