inclusionAI / Ling-3.0-tiny-singprobe

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Introduction of Ling-3.0-tiny-singprobe

Model Details of Ling-3.0-tiny-singprobe

Ling3-SingProbe

English | 中文

Model Description

SingProbe is an intrinsic streaming guardrail built on inclusionAI/Ling-3.0-tiny . Rather than running a separate safety model, this lightweight probe reuses the base model's hidden states during generation to score, at every token, query intent , response unsafety , and hallucination risk . It adds less than 0.5% decode-time overhead.

Base model Probe parameters Tapped layers Outputs
inclusionAI/Ling-3.0-tiny-singprobe 3.22M [6, 14, 22] 8 intents + unsafe + hallucination

See the technical report for methodology and complete results; implementation details are available at inclusionAI/SingProbe .

Evaluation

Higher is better for every metric. Results are averages over the benchmark suites specified below.

Task Metric Ling-3.0-tiny-singprobe Reference baseline
Query intent classification (6 benchmarks) F1 0.8561 Qwen3Guard-Stream-8B-strict: 0.8602
Response safety classification (8 benchmarks) F1 0.8508 Qwen3Guard-Stream-8B-strict: 0.8486
Streaming safety (3 benchmarks) R-AUC / T-AUC 0.9888 / 0.9479 Qwen3Guard-Stream-8B-strict: 0.9640 / 0.8893
Hallucination detection (6 benchmarks) AUC 0.7765 DRIFT: 0.7408
Deployment characteristic Result
Benign-response false-positive rate 0.07% average across 5 datasets
Online hallucination detection 0.6807 average AUC under free generation
Decode overhead < 0.5%
Quick Start

SingProbe is supported through the SGLang integration branch or vLLM integration branch . Load the probe by its Hugging Face ID at server launch:

python -m sglang.launch_server \
  --model-path inclusionAI/Ling-3.0-tiny \
  --probe-ckpt inclusionAI/Ling-3.0-tiny-singprobe \
  --port 30000

The integrations return one score dictionary per generated token ( label_0 – label_9 ). They currently support Ling-3.0 ( BailingMoeV3ForCausalLM ) base models only. Use the exact base-model/probe pair: inclusionAI/Ling-3.0-tiny with this checkpoint.

Citation
@article{singteam2026singprobe,
  title = {SingProbe Technical Report},
  author = {Sing Team},
  journal = {arXiv preprint arXiv:2608.30703},
  year = {2026},
}

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