Instructions to use xraychen/mqa-sim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xraychen/mqa-sim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="xraychen/mqa-sim")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("xraychen/mqa-sim") model = AutoModelForQuestionAnswering.from_pretrained("xraychen/mqa-sim") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 394f6b54e774941772b8960e0ef63d6f02bcf081a0ee16bbdb2cc78e5e473a2a
- Size of remote file:
- 55.8 MB
- SHA256:
- 191c1e05a9830aa840adff5200fafc983882cb7a3bc58c5913d6a6648cbff14c
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.