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:
- 8c35b8ff82557941151b0f1a888eeab8ffc3911d14e5db7c76a343805c96cde0
- Size of remote file:
- 33 MB
- SHA256:
- c9249f5cd2670253df45efc8346ccb531eb88ad9fe165f421c7bcdfac18ce0e2
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