Text Classification
Transformers
Safetensors
decision-model
classification
julia
open-jev
head-finetune
low-resource
Instructions to use SHSLab/Qyvos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SHSLab/Qyvos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SHSLab/Qyvos")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SHSLab/Qyvos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Qyvos v1: Julia-1 backbone (bit-exact) + Open-Jev head fine-tune (30k rows, low-RAM protocol)
31f7037 verified Download julia/router/__init__.py from SHSLab/Qyvos: direct link, hf CLI and curl.
- Browser
- Download file 357 Bytes
-
https://huggingface.co/SHSLab/Qyvos/resolve/main/julia/router/__init__.py
- Command line
-
hf download hf://SHSLab/Qyvos/julia/router/__init__.py
-
curl -L -o __init__.py https://huggingface.co/SHSLab/Qyvos/resolve/main/julia/router/__init__.py
357 Bytes
| """Python-compatible Julia router powered by compiled Bend trees.""" | |
| from .native import BendReducer | |
| from .router import Router, RouteResult | |
| __all__ = ['BendReducer', 'Router', 'RouteResult', 'FastEngine'] | |
| def __getattr__(name): | |
| if name == 'FastEngine': | |
| from .engine import FastEngine | |
| return FastEngine | |
| raise AttributeError(name) | |