Instructions to use CompassioninMachineLearning/Pretrained_twentyK_cleanChat_split_output_instr_masking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CompassioninMachineLearning/Pretrained_twentyK_cleanChat_split_output_instr_masking with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CompassioninMachineLearning/Pretrained_twentyK_cleanChat_split_output_instr_masking", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec4c2aba2d3169c171bbc69061ee9b9ad6ef1c12f427ef65c1ec9928e0ebb5dc
- Size of remote file:
- 54.6 MB
- SHA256:
- eac158dea675cda058294163e1ce4c9c930b26b4032b389207b0f4153b43972d
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