Instructions to use hf-internal-testing/tiny-random-FlaubertModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FlaubertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-FlaubertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-FlaubertModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-FlaubertModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03c295a18b0f53ee4427e4f80c359b8c8e55f32f307fcff48029c4f2306ea747
- Size of remote file:
- 8.99 MB
- SHA256:
- 1e3d19073e3563867edbac8a314f5ca3a96b0509379c3ed004858b9630cf2a79
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.