Instructions to use hf-internal-testing/tiny-random-XLMModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-XLMModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-XLMModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XLMModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-XLMModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 83219bf05dcaab5ff35eec74ce4fc1e7d9dc648e7d0bffa9d72c33ae7ebe144c
- Size of remote file:
- 4.21 MB
- SHA256:
- fad509400e51266cb46df1e205e7233e0b613f231746b1652c1fbe760dbd3ccf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.