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
File size: 619 Bytes
31f7037 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | [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"]
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