Zero-Shot Classification
Transformers
Safetensors
qwen3_5
feature-extraction
decision-model
classification
system-one
multimodal
vision
video
custom_code
Eval Results
Instructions to use vllm-sr/d3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/d3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="vllm-sr/d3", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("vllm-sr/d3", trust_remote_code=True) model = AutoModel.from_pretrained("vllm-sr/d3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Card: serve with --max-model-len 32768 in Run with vLLM
#7
by subin - opened
At the default 262,144 positions, vLLM's startup profiling pass for d3 does not fit on an 80 GB GPU, even with d3-FP8 (discussion #2). The serve command now passes --max-model-len 32768, which covers d3's requests, and the section says why.
Xunzhuo changed pull request status to merged