Instructions to use hf-tiny-model-private/tiny-random-XLMModel 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-XLMModel 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-XLMModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLMModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-XLMModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3fb7294f486a160766464c2011e9e63fd3c1d5d8ff7450812de2fb9612b50a4
- Size of remote file:
- 4.21 MB
- SHA256:
- 52fd0c5f544a0533a6f3b6f36bb42db531489ce2e602b9d0640e52552cd96fe6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.