Zero-Shot Classification
Transformers
Safetensors
qwen3_5
feature-extraction
auto-round
intel
quantization
4bit
decision-model
multimodal
vision
video
vllm-semantic-router
custom_code
4-bit precision
Instructions to use Vishva007/d3-mini-W4A16-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vishva007/d3-mini-W4A16-AutoRound with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Vishva007/d3-mini-W4A16-AutoRound", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Vishva007/d3-mini-W4A16-AutoRound", trust_remote_code=True) model = AutoModel.from_pretrained("Vishva007/d3-mini-W4A16-AutoRound", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle