Instructions to use MLMvsCLM/1b-mlm30-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/1b-mlm30-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/1b-mlm30-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/1b-mlm30-42k", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 423b71ad97e9fb9f37f418b8de58f79fbf8fb49511668074e3e3ca17a5be6238
- Size of remote file:
- 5.64 GB
- SHA256:
- 60d2c9231e0587955021aab6a50d549e1466149d3653c8c50b8592c0a399d70d
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