Instructions to use hf-tiny-model-private/tiny-random-GPTJModel 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-GPTJModel 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-GPTJModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-GPTJModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-GPTJModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 324ff22b346e92f65f1b1c977882e422cf1fe2051baf3baf7227ef292fed9f02
- Size of remote file:
- 460 kB
- SHA256:
- c455e3cf5a03c11034d8ceda57290f7eb5d99251da09f4f6a0ffd771975612cd
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