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-lite-W4A16-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vishva007/d3-lite-W4A16-AutoRound with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Vishva007/d3-lite-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-lite-W4A16-AutoRound", trust_remote_code=True) model = AutoModel.from_pretrained("Vishva007/d3-lite-W4A16-AutoRound", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Vishva007/d3-lite-W4A16-AutoRound: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/Vishva007/d3-lite-W4A16-AutoRound/resolve/main/tokenizer.json
- Command line
-
hf download hf://Vishva007/d3-lite-W4A16-AutoRound/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Vishva007/d3-lite-W4A16-AutoRound/resolve/main/tokenizer.json
20 MB
- Xet hash:
- e558cdad9a88d177076f9525ed4ef2d55dfd5dafafd6baac7c6105590ab49b7b
- Size of remote file:
- 20 MB
- SHA256:
- a5cd9732badce41de57e6efce8302930ded1c1188c5f81feb2bd6c24c4a1941f
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