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 pyproject.toml from SHSLab/Qyvos: direct link, hf CLI and curl.
- Browser
- Download file 619 Bytes
-
https://huggingface.co/SHSLab/Qyvos/resolve/main/pyproject.toml
- Command line
-
hf download hf://SHSLab/Qyvos/pyproject.toml
-
curl -L -o pyproject.toml https://huggingface.co/SHSLab/Qyvos/resolve/main/pyproject.toml
619 Bytes
| [build-system] | |
| requires = ["setuptools>=70"] | |
| build-backend = "setuptools.build_meta" | |
| [project] | |
| name = "supersonic-julia" | |
| version = "0.1.0" | |
| requires-python = ">=3.11" | |
| dependencies = ["torch>=2.6", "transformers>=5.0,<5.1", "safetensors>=0.5", "numpy>=1.26"] | |
| [tool.setuptools.packages.find] | |
| include = ["julia*"] | |
| exclude = ["julia.router.tests*"] | |
| [project.optional-dependencies] | |
| benchmark = ["pyarrow>=15"] | |
| cuda = ["bitsandbytes>=0.48,<0.50", "accelerate>=1.10,<2"] | |
| [tool.setuptools.package-data] | |
| "julia.router" = ["native/*.bend", "native/*.c", "README.md"] | |
| "julia" = ["native/*.c", "native/*.bend", "native/*.json"] | |