Instructions to use MLMvsCLM/1b-clm-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/1b-clm-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/1b-clm-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/1b-clm-42k", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20603461cf89d57125f5f5b43a777d157d60e089d6d8acb139c6e84a0e2522de
- Size of remote file:
- 5.64 GB
- SHA256:
- 82c5093d537e142fca939cbc979cf4e92ae9f7d5c224d75023ac9ee2cff58ec2
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