Instructions to use quancute/tmp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quancute/tmp with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("quancute/tmp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download tokenizer.json from quancute/tmp: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/quancute/tmp/resolve/main/tokenizer.json
- Command line
-
hf download hf://quancute/tmp/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/quancute/tmp/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 138eeecba70d195a4c50504bc1ed95999b49814f766b680d4f60c2868c9d204d
- Size of remote file:
- 11.4 MB
- SHA256:
- 741c85ffe434aad73e934a73ef380c85e94cd863b8f55e1a1ad66cacb5a93dfd
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