Instructions to use MLMvsCLM/610m-clm-42k-40000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/610m-clm-42k-40000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/610m-clm-42k-40000", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/610m-clm-42k-40000", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 959ffade6ca138f855be774efd567c413f2c09f357110fcfff31df15ae48c058
- Size of remote file:
- 3.02 GB
- SHA256:
- b8d228d684a0c07152adcad8bda4f1b5dd400ee7b3ff8a51dded4989164b726f
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