Instructions to use hf-tiny-model-private/tiny-random-XGLMModel 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-XGLMModel 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-XGLMModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XGLMModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-XGLMModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80e1827625b230254d08c09248002e5e73cc3bf28467311eeba9b8c4aa27327f
- Size of remote file:
- 33 MB
- SHA256:
- d7f790ea8762b3eccb2d03b5b79aa3dc49c09bebfd57c458054ce90e8d9304a6
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