Instructions to use devngho/llama3-jamo-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devngho/llama3-jamo-tokenizer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("devngho/llama3-jamo-tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from devngho/llama3-jamo-tokenizer: direct link, hf CLI and curl.
- Browser
- Download file 21.4 MB
-
https://huggingface.co/devngho/llama3-jamo-tokenizer/resolve/main/tokenizer.json
- Command line
-
hf download hf://devngho/llama3-jamo-tokenizer/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/devngho/llama3-jamo-tokenizer/resolve/main/tokenizer.json
21.4 MB
- Xet hash:
- ada2dcdc458ce0c5fefcbaeba12d984e4db7e3ce0fe9849a7b14c9a077b4e7a3
- Size of remote file:
- 21.4 MB
- SHA256:
- bc92401b4117725316469dcf552018826e56e575da3cf03f0f502d7c187c3683
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