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-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/d3-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="vllm-sr/d3-lite", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("vllm-sr/d3-lite", trust_remote_code=True) model = AutoModel.from_pretrained("vllm-sr/d3-lite", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add Typed Decisions benchmark eval results
#2
by SaylorTwift HF Staff - opened
accuracy, kl_from_gold, brier, ece on LocalLLaMA/typed-decisions test split (400 cases, 2000 decisions, one request per case). Independent run 2026-10-16; details: https://huggingface.co/buckets/SaylorTwift/typed-decisions-evals
Xunzhuo changed pull request status to merged