Instructions to use MLMvsCLM/610m-mlm30-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/610m-mlm30-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/610m-mlm30-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/610m-mlm30-42k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 72613020ee907ac2d23ac7b69dc2594971e0e0a74ac4f231e6168389ca01e5ec
- Size of remote file:
- 3.02 GB
- SHA256:
- 871785db4cb3782bf7401587b840d3a4e4a74892827d45b5aad591e05828a4dc
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