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:
- dea9caa837a14beba6c0cf26e880c8ef7fcfaa85a798ce4dea656c1cfe5c414e
- Size of remote file:
- 338 kB
- SHA256:
- 7b811c639080bac8960d30ccca7b81717bc997c67ad443f2d07c7860a1c7d953
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.