Instructions to use MLMvsCLM/610m-clm-12k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/610m-clm-12k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/610m-clm-12k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/610m-clm-12k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 41907f675ac2dfcfe49b2139f317b9559ba7d02af919b7662df2e0487d87ed89
- Size of remote file:
- 3.02 GB
- SHA256:
- 93e9c0e25eecd60596908399c50576479e2c430a0531265c556df928e84da1aa
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