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")# 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 config.json from SHSLab/Qyvos: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/SHSLab/Qyvos/resolve/main/config.json
- Command line
-
hf download hf://SHSLab/Qyvos/config.json
-
curl -L -o config.json https://huggingface.co/SHSLab/Qyvos/resolve/main/config.json
296 Bytes
| { | |
| "format_version": 1, | |
| "architecture": "JuliaDecisionModel", | |
| "julia_config_file": "julia_config.json", | |
| "encoder_config_file": "encoder/config.json", | |
| "weights_file": "model.safetensors", | |
| "tokenizer_directory": "tokenizer", | |
| "name": "Qyvos", | |
| "base_model": "SupersonicLabs/Julia-1" | |
| } | |