Zero-Shot Classification
MLX
Safetensors
qwen3_5
oq
quantized
d3
decision-model
systemone
custom_code
4-bit precision
Instructions to use Jundot/d3-oQ4e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Jundot/d3-oQ4e with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download Jundot/d3-oQ4e --local-dir d3-oQ4e
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download tokenizer.json from Jundot/d3-oQ4e: direct link, hf CLI and curl.
- Browser
- Download file 12.8 MB
-
https://huggingface.co/Jundot/d3-oQ4e/resolve/main/tokenizer.json
- Command line
-
hf download hf://Jundot/d3-oQ4e/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Jundot/d3-oQ4e/resolve/main/tokenizer.json
12.8 MB
- Xet hash:
- ddca7e708ff0da8902fdcef5209d5eea8bf922715733eb02f93026a37478e426
- Size of remote file:
- 12.8 MB
- SHA256:
- 0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.