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
Download adapter/adapter.json from BoldingBuilds/Wildcat-Mini: direct link, hf CLI and curl.
- Browser
- Download file 220 Bytes
-
https://huggingface.co/BoldingBuilds/Wildcat-Mini/resolve/main/adapter/adapter.json
- Command line
-
hf download hf://BoldingBuilds/Wildcat-Mini/adapter/adapter.json
-
curl -L -o adapter.json https://huggingface.co/BoldingBuilds/Wildcat-Mini/resolve/main/adapter/adapter.json
220 Bytes
| { | |
| "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" | |
| } |