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", device_map="auto")# 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:
- 9f0ba34deb07e7df65af989b07e49b2269ff2d96ca68254db10c7f65fa91e164
- Size of remote file:
- 338 kB
- SHA256:
- c5664a813626faa1773f7b0c19c274e2a4396dd9e3e3e9197637ed8b6bc41a26
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