Instructions to use hf-internal-testing/tiny-random-FalconForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FalconForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-internal-testing/tiny-random-FalconForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-FalconForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-FalconForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a2b6c634776d88e05cd449964ebff31b18ecf66f42f4275e70d027078e45103
- Size of remote file:
- 233 kB
- SHA256:
- 51ed4227528bce8c8e511ebe5759ad3c254ab777505ec1a04d605b3eac5605a6
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.