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:
- c96aa16d9e8d62e1f328837fa5bfcbe1c308adfa34b795b1c38d102f8c77e7c6
- Size of remote file:
- 233 kB
- SHA256:
- 81ca5883a698406efc992a0abde663c2337ee64601a1d87082e2f6d81f176f7a
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.