Zero-Shot Classification
Transformers
qwen3_5
feature-extraction
decision-model
system-one
classification
3-bit
hqq
lora
custom_code
Instructions to use BoldingBuilds/Wildcat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BoldingBuilds/Wildcat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="BoldingBuilds/Wildcat", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("BoldingBuilds/Wildcat", trust_remote_code=True) model = AutoModel.from_pretrained("BoldingBuilds/Wildcat", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 422 Bytes
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Copyright 2026 vLLM Semantic Router Team
Wildcat
A 3-bit, LoRA-repaired derivative of d3-ultra by BoldingBuilds.
The repair LoRA learned from the answers of Fastino GLiDE, d3-ultra and Jeffan.
Wildcat is a Derivative Work under the Fastino Community License (LICENSES/FASTINO-LICENSE.txt).
Built with Fastino GLiDE.
GLiDE notice: LICENSES/GLiDE-NOTICE. Qwen license: LICENSES/Qwen3.8-27B-LICENSE.
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