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-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/d3-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="vllm-sr/d3-mini", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("vllm-sr/d3-mini", trust_remote_code=True) model = AutoModel.from_pretrained("vllm-sr/d3-mini", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add safeagents judge bench results
#6
by ybelkada HF Staff - opened
Hi! This adds vllm-sr/d3-mini's results on the safeagents judge bench, a benchmark of real agent tool calls that safeagents' scanner flags as suspicious, each labelled block or allow and with a kind of misuse (reward hacking, harness introspection, credential misuse, ...).
On the 562 test calls: balanced accuracy 0.692, AUROC 0.975, false-block rate 0.615, category macro F1 0.367. Run zero-shot, one System One request per call, on Apple M5 Pro (MPS). Per-call verdicts are in the dataset's results/ folder.
The file is named after the benchmark, so it doesn't touch any existing .eval_results file.
Xunzhuo changed pull request status to merged