Instructions to use MLMvsCLM/610m-clm-40k-mlm30-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/610m-clm-40k-mlm30-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/610m-clm-40k-mlm30-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/610m-clm-40k-mlm30-42k", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ef8002a58cd53e29d5f98e943764cb41dbd363661fab3c42c42ab886e077a40
- Size of remote file:
- 3.02 GB
- SHA256:
- 331f008f03b650fabad3456eb63da129be5fade778cf28f60b7da84b9767e8c4
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