Instructions to use MLMvsCLM/210m-clm-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/210m-clm-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/210m-clm-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/210m-clm-42k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5da4fe43357cdec96325ee732e5747f0268e23199e18ad9d7580cf2f46663d0e
- Size of remote file:
- 1.24 GB
- SHA256:
- 735a0115d6e00b15d0040173061a619e7beb8b0c08fc9e291a4a95d51ca5fb83
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.