Instructions to use hf-internal-testing/tiny-random-SplinterModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SplinterModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-SplinterModel")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SplinterModel") model = AutoModelForMultimodalLM.from_pretrained("hf-internal-testing/tiny-random-SplinterModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 426988dd1fd4c60ac48607dc3e81c47c52f0eb1c0a55baee14241a061f668784
- Size of remote file:
- 3.95 MB
- SHA256:
- 0bf7bf6e8bef5d5a0329b1f793cfb8b4ffe70f5eeea743e34d2bf9e0136009e0
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