Instructions to use MLMvsCLM/610m-clm-3k-mlm40-12k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/610m-clm-3k-mlm40-12k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/610m-clm-3k-mlm40-12k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/610m-clm-3k-mlm40-12k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73c148456372f717fc5a98a9fb73cf3c380a3e22587f18febfe3b37f4fd10b29
- Size of remote file:
- 3.02 GB
- SHA256:
- 41ebe4d698379417df7eb6698d9b3835a63f178d4f1cd684fb84559c4e5ea763
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