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 tokenizer/tokenizer.json from SHSLab/Qyvos: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/SHSLab/Qyvos/resolve/main/tokenizer/tokenizer.json
- Command line
-
hf download hf://SHSLab/Qyvos/tokenizer/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/SHSLab/Qyvos/resolve/main/tokenizer/tokenizer.json
34.4 MB
- Xet hash:
- 8fdbdb0307b65058d3b81a88cba0082cd1bbe8e85d34295d2271d6fa85a31c7a
- Size of remote file:
- 34.4 MB
- SHA256:
- 609d8f4c067cd3950f88594c5a802616cea245823836ef5848ee4fc40aab5b6f
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