Instructions to use togethercomputer/m2-bert-80M-2k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use togethercomputer/m2-bert-80M-2k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="togethercomputer/m2-bert-80M-2k", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("togethercomputer/m2-bert-80M-2k", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1fe293228f33f62e6c880ed614c375a13202780463c9ff8933284f1203dfe2f3
- Size of remote file:
- 328 MB
- SHA256:
- afd8cf8bdbc6727345f1f28a8791152368d1f34f3a9e5a96114cc155770a73e6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.