Instructions to use MLMvsCLM/610m-clm-42k-2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/610m-clm-42k-2000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/610m-clm-42k-2000", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/610m-clm-42k-2000", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13c11b68da0fd96792662adb3cab16ea256132934509763e04fb0723730291fb
- Size of remote file:
- 3.02 GB
- SHA256:
- d9f88e0a6b2220bfa190aa60a01c669d25062b23954a1454d792a2a9821d748a
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