Instructions to use MLMvsCLM/610m-mlm40-42k-10000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/610m-mlm40-42k-10000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/610m-mlm40-42k-10000", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/610m-mlm40-42k-10000", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee9188bdca1552b78b3ae14cca8f600debd8a7c640f4f28043a2479dbe964188
- Size of remote file:
- 3.02 GB
- SHA256:
- 698f8ff9dc06b5c5325b75c93c90c8fd00ecdf2953808535814bc9b97614e574
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