Instructions to use MLMvsCLM/610m-clm-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/610m-clm-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/610m-clm-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/610m-clm-42k", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03600401a14c925ff8035fd1dc7721bccc40186e8e91cbb78c6ad6ab3a11d2ec
- Size of remote file:
- 3.02 GB
- SHA256:
- 1d8388c26342d523b5a4ad3006c5c2be7bc9a05346685b96953581227f7645c3
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