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
File size: 257 Bytes
49d61d8 | 1 2 3 4 5 6 7 8 9 10 11 | {
"bits": 4,
"data_type": "int",
"group_size": 32,
"sym": true,
"iters": 1200,
"autoround_version": "0.16.0",
"block_name_to_quantize": "model.language_model.layers",
"quant_method": "auto-round",
"packing_format": "auto_round:auto_gptq"
} |