Instructions to use hf-internal-testing/tiny-random-LxmertModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LxmertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-LxmertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LxmertModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-LxmertModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f31e718ffa1567d0afbd86a4b0177c5aad202a2ea0507ca4fd974240cd13bc46
- Size of remote file:
- 499 kB
- SHA256:
- 3dc1b97820650df10087d1b45c289e9a762c6fb033f5805a5349822ea713a325
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.