tags:
- safety
- guardrail
- hallucination-detection
- streaming
- ling-3.0
license: mit
base_model: inclusionAI/Ling-3.0-tiny
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},
}