Zero-Shot Classification
Transformers
Safetensors
qwen3_5
feature-extraction
decision-model
classification
system-one
multimodal
vision
video
object-detection
custom_code
Instructions to use vllm-sr/d3-ultra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/d3-ultra with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="vllm-sr/d3-ultra", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("vllm-sr/d3-ultra", trust_remote_code=True) model = AutoModel.from_pretrained("vllm-sr/d3-ultra", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Serving with vllm-sr
#2
by xfalcox - opened
Why this model doesn't have instruction on how to serve with vllm-sr ?
Thanks for asking. d3-ultra does run on the vLLM Semantic Router model runtime; the card is missing that section. One catch for now: the runtime's built-in entry for d3-ultra still points at a pre-release revision that is no longer on the Hub, so vllm-srun serve vllm-sr/d3-ultra without a revision fails with "Revision Not Found". Until that is fixed, pass the current revision explicitly:
pip install -e "src/model-runtime[multimodal]" flash-linear-attention # in a checkout of vllm-project/semantic-router, main
vllm-srun serve vllm-sr/d3-ultra --revision 52ba9e7a00402f9560c7fba8c039608e7ddee7fb --port 8899 --max-request-bytes 67108864
We will fix the built-in entry and add the serving section to the card.