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: 220 Bytes
5889269 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"r": 16,
"lora_alpha": 32,
"target_modules": [
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj",
"in_proj_qkv",
"in_proj_z",
"out_proj"
],
"scope": "language_model"
} |