Instructions to use hf-tiny-model-private/tiny-random-MvpModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-MvpModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-MvpModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-MvpModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-MvpModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dfa32a890187766cad14ec3ddb57190880002678322b9e9f457f7ef60bc0758c
- Size of remote file:
- 137 kB
- SHA256:
- 6cd709d09068d5b0aa7dbc5b639d11e11e993820508ed4aab3ebba61cc6de7c0
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