Zero-Shot Classification
Transformers
Safetensors
qwen3_5
feature-extraction
decision-model
classification
system-one
multimodal
vision
maincode
matilda
custom_code
Instructions to use Maincode/Matilda-Decision-v1-Edge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/Matilda-Decision-v1-Edge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Maincode/Matilda-Decision-v1-Edge", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Maincode/Matilda-Decision-v1-Edge", trust_remote_code=True) model = AutoModel.from_pretrained("Maincode/Matilda-Decision-v1-Edge", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 285 Bytes
90c2cee | 1 2 3 4 5 6 7 8 | transformers==5.17.0 flash-linear-attention==0.5.2 safetensors==0.8.0 pillow==12.3.0 numpy==2.5.3 # Also install torch==2.14.0 and torchvision==0.29.0 for your accelerator (validated on ROCm 7.2, AMD Instinct MI355X). # Optional, for the Decision Index kit engine: decision-index==0.3 |