Ling-3.0-tiny-singprobe

inclusionAI/Ling-3.0-tiny-singprobe

Ling-3.0-tiny-singprobe is a machine learning model from inclusionAI. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.

<p align="center"<bEnglish</b | <a href="https://huggingface.co/inclusionAI/Ling-3.0-tiny-singprobe/blob/main/READMECN.md"中文</a</p

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At a glance

License: mit
Model type: sing_probe_attn
Access: Public
Created: Sep 1, 2026
Updated: Sep 1, 2026
SHA: 53fba6b8

Base models

inclusionAI/Ling-3.0-tiny

Model card

From the Hugging Face model README

Ling3-SingProbe

<p align="center"><b>English</b> | <a href="https://huggingface.co/inclusionAI/Ling-3.0-tiny-singprobe/blob/main/README_CN.md">中文</a></p>

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},
}

Type: sing_probe_attn
License: mit
Created: Sep 1, 2026
Updated: Sep 1, 2026