Zero-Shot Classification
Transformers
qwen3_5
feature-extraction
decision-model
system-one
classification
ternary
2-bit
lora
custom_code
Instructions to use BoldingBuilds/Wildcat-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BoldingBuilds/Wildcat-Mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="BoldingBuilds/Wildcat-Mini", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("BoldingBuilds/Wildcat-Mini", trust_remote_code=True) model = AutoModel.from_pretrained("BoldingBuilds/Wildcat-Mini", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 133 Bytes
5889269 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3
size 12809320
|