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:
- 3dfe02b0eb9187761784fe8b792f0b9e81f444f0a94bc6c989c7100527e627f0
- Size of remote file:
- 338 kB
- SHA256:
- 32a4b97acf5cfb07a0f2bdf9f0c952c756ed4e0abe09872fd72f861822e87142
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