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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5f976f9595ff6ee287a94c241ea26e17b2cbe38ab0f2d12f9ae735ef9e31f4e
- Size of remote file:
- 33.1 MB
- SHA256:
- 20e6e67a17fc601af3c1b7ab33a8ee662d84c281f77afb3feb6d3ce34d404e7d
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