Instructions to use MLMvsCLM/610m-clm-40k-mlm20-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/610m-clm-40k-mlm20-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/610m-clm-40k-mlm20-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/610m-clm-40k-mlm20-42k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f285d7ab64345ef239217554d61e9c2151ab1c8f0e991b913e0cb168081b059
- Size of remote file:
- 3.02 GB
- SHA256:
- 53244249c62196ce7a435a7c17356c61c72d63b6426cfc2f1bfd6fc996da4659
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