Instructions to use Backup-CaML/Pretrained_twentyK_cleanChat_split_with_instr_masking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Backup-CaML/Pretrained_twentyK_cleanChat_split_with_instr_masking with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Backup-CaML/Pretrained_twentyK_cleanChat_split_with_instr_masking", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Backup-CaML/Pretrained_twentyK_cleanChat_split_with_instr_masking: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/Backup-CaML/Pretrained_twentyK_cleanChat_split_with_instr_masking/resolve/main/tokenizer.json
- Command line
-
hf download hf://Backup-CaML/Pretrained_twentyK_cleanChat_split_with_instr_masking/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Backup-CaML/Pretrained_twentyK_cleanChat_split_with_instr_masking/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- c4530699d0209a307783c8d39f35cd73dc23b5429abaaa6604036ce2b1b2ebf2
- Size of remote file:
- 17.2 MB
- SHA256:
- 9c85066e7642934ed09b44155e6566b0b5dab2637fb9433439ba5c9c7f8b50d3
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