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:
- 9563b1d4863cb290602e6f9ea3a376647d1b5c5508a610d3f68fffa2301de29a
- Size of remote file:
- 233 kB
- SHA256:
- 7d2cf6750f9f937cc091018fb4be34854d9d7c02c36a2a9f5296ea0528486b7b
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.