Instructions to use togethercomputer/m2-bert-80M-8k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use togethercomputer/m2-bert-80M-8k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="togethercomputer/m2-bert-80M-8k", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("togethercomputer/m2-bert-80M-8k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4eac4560a99a6df601127f74b56ac2b80760e19ff02d54b6936b2f73bdf2c202
- Size of remote file:
- 351 MB
- SHA256:
- a7e37c447d5fec5032e5bb202152285809f78a5c8e33ad68d8cc04e16b88995a
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