Transformers
tokenizer
bpe
byte-level-bpe
multilingual
code
python
latex
small-language-models
embedding-efficiency
flores-200
tokenizerbench
chat-template
fill-in-the-middle
Instructions to use JamesQuartz/QT-VII-Tokenizer-Family with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JamesQuartz/QT-VII-Tokenizer-Family with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JamesQuartz/QT-VII-Tokenizer-Family", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download benchmark_charts/1_TokenizerBench_Suite_Comparison.png from JamesQuartz/QT-VII-Tokenizer-Family: direct link, hf CLI and curl.
- Browser
- Download file 222 kB
-
https://huggingface.co/JamesQuartz/QT-VII-Tokenizer-Family/resolve/main/benchmark_charts/1_TokenizerBench_Suite_Comparison.png
- Command line
-
hf download hf://JamesQuartz/QT-VII-Tokenizer-Family/benchmark_charts/1_TokenizerBench_Suite_Comparison.png
-
curl -L -o 1_TokenizerBench_Suite_Comparison.png https://huggingface.co/JamesQuartz/QT-VII-Tokenizer-Family/resolve/main/benchmark_charts/1_TokenizerBench_Suite_Comparison.png
222 kB

- Xet hash:
- f2ff28909fee38231532570c10af1dd513fef80ad7c5133cbb491c479627c19b
- Size of remote file:
- 222 kB
- SHA256:
- 6f6ca73cef90f00c62f6da66f2d0aba7269ca4a68d3d02ae768cd34bad65d7c7
·
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