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:
- 239ddac55727b1943bcfcff5e907bf824e592e534f7c578808a88d17f4e73417
- Size of remote file:
- 17.2 MB
- SHA256:
- 4c48025c56c78c2e1805007e6e068bf61ceedce91484eae064957cb865dcfec2
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