inclusionAI/Ling-3.0-flash-singprobe

Ling3-SingProbe

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Model Description

SingProbe is an intrinsic streaming guardrail for inclusionAI/Ling-3.0-flash-singprobe, built on inclusionAI/Ling-3.0-flash. 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-flash-singprobe | 5.18M | [13, 26, 40] | 8 intents + unsafe + hallucination |

See the technical report for methodology and complete results. Training codes 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-flash-singprobe | Reference baseline | | --- | --- | ---: | ---: | | Query intent classification (6 benchmarks) | F1 | 0.8674 | YuFeng-XGuard-Reason-8B: 0.8714 | | Response safety classification (8 benchmarks) | F1 | 0.8728 | Qwen3Guard-Gen-8B-strict: 0.8604 | | Streaming safety (3 benchmarks) | R-AUC / T-AUC | 0.9887 / 0.9481 | Qwen3Guard-Stream-8B-strict: 0.9640 / 0.8893 | | Hallucination detection (6 benchmarks) | AUC | 0.8012 | DRIFT: 0.8000 |

| Deployment characteristic | Result | | --- | --- | | Benign-response false-positive rate | 0.03% average across 5 datasets | | Online free-generation performance | 0.9641 accuracy / 0.6452 F1 (leave-one-out) | | 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-flash \
--probe-ckpt inclusionAI/Ling-3.0-flash-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-flash with this checkpoint.

Citation

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