Zero-Shot Classification
Transformers
Safetensors
English
qwen3_5
feature-extraction
ollaya
sura
decision
classification
distillation
Instructions to use ollaya-dev/sura-0.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ollaya-dev/sura-0.8b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="ollaya-dev/sura-0.8b")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("ollaya-dev/sura-0.8b") model = AutoModel.from_pretrained("ollaya-dev/sura-0.8b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download readout.safetensors from ollaya-dev/sura-0.8b: direct link, hf CLI and curl.
- Browser
- Download file 522 kB
-
https://huggingface.co/ollaya-dev/sura-0.8b/resolve/main/readout.safetensors
- Command line
-
hf download hf://ollaya-dev/sura-0.8b/readout.safetensors
-
curl -L -o readout.safetensors https://huggingface.co/ollaya-dev/sura-0.8b/resolve/main/readout.safetensors
522 kB
- Xet hash:
- 6dabc226d636c5a446a8446801a9e711fcde74044fb3828b7d0ff75117973c21
- Size of remote file:
- 522 kB
- SHA256:
- d51e8cdec401f10813eacba62198dd47a09884ef5a8a0599545234b925821f5c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.