Instructions to use MLMvsCLM/610m-clm-40k-mlm50-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/610m-clm-40k-mlm50-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/610m-clm-40k-mlm50-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/610m-clm-40k-mlm50-42k", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11f3b58dea29e9db665aa2abbbe0c80144d4d8cb492d62779f5aac01fbca2342
- Size of remote file:
- 3.02 GB
- SHA256:
- 1597bd703cce10abecdee00a01fbbd51ef8709e79a71e20e5216c6fb9c8024f7
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