---
title: DeepSeek-V4-Flash-0731-singprobe
canonical_url: "https://www.modelscope.ai/models/inclusionAI/DeepSeek-V4-Flash-0731-singprobe"
md_url: "https://www.modelscope.ai/models/inclusionAI/DeepSeek-V4-Flash-0731-singprobe.md"
repository: inclusionAI/DeepSeek-V4-Flash-0731-singprobe
last_updated: 2026-09-15
license: mit
model_type:
  - sing_probe_attn
architectures:
  - SingProbeAttnModel
base_model:
  - deepseek-ai/DeepSeek-V4-Flash-0731
base_model_relation: finetune
parameters: 8.1M
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 62
stars: 0
tags:
  - safety
  - guardrail
  - hallucination-detection
  - streaming
  - deepseek-v4
---

# DeepSeek-V4-Flash-0731-singprobe

> DeepSeek-V4-Flash-0731-singprobe - An open-source model by inclusionAI on ModelScope. DeepSeek-V4-Flash-0731-singprobe

inclusionAI/DeepSeek-V4-Flash-0731-singprobe is a 8.1M-parameter machine learning model on ModelScope. licensed under mit. derived from deepseek-ai/DeepSeek-V4-Flash-0731.

- **Repository**: inclusionAI/DeepSeek-V4-Flash-0731-singprobe
- **License**: mit
- **Parameters**: 8.1M
- **Base model**: deepseek-ai/DeepSeek-V4-Flash-0731
- **Tags**: safety, guardrail, hallucination-detection, streaming, deepseek-v4
- **Downloads**: 62
- **Stars**: 0
- **Last updated**: 2026-09-15

Source: https://www.modelscope.ai/models/inclusionAI/DeepSeek-V4-Flash-0731-singprobe

---

# DeepSeek-V4-Flash-0731-singprobe

## Model Description

SingProbe is an **intrinsic streaming guardrail** built on `deepseek-ai/DeepSeek-V4-Flash-0731`. 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/DeepSeek-V4-Flash-0731-singprobe` | 8.13M | `[13, 27, 41]` | 8 intents + unsafe + hallucination |

See the [technical report](https://arxiv.org/abs/2608.30703) for methodology and complete results. Training codes are available at [inclusionAI/SingProbe](https://github.com/inclusionAI/SingProbe).

## Evaluation

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

| Task | Metric | DeepSeek-V4-Flash-0731-singprobe | Reference baseline |
| --- | --- | ---: | ---: |
| Query intent classification (6 benchmarks) | F1 | **0.8811** | YuFeng-XGuard-Reason-8B: 0.8714 |
| Response safety classification (8 benchmarks) | F1 | **0.8697** | Qwen3Guard-Gen-8B-strict: 0.8604 |
| Streaming safety (3 benchmarks) | R-AUC / T-AUC | **0.9882 / 0.9508** | Qwen3Guard-Stream-8B-strict: 0.9640 / 0.8893 |
| Hallucination detection (6 benchmarks) | AUC | **0.7949** | DRIFT: 0.8000 |

| Deployment characteristic | Result |
| --- | --- |
| Benign-response false-positive rate | 0.01% average across 5 datasets |
| Decode overhead | < 0.5% |

## Quick Start

SingProbe is supported through the [SGLang integration branch](https://github.com/jinzhen-lin/sglang/tree/token-probe-ling3-flash-main) or [vLLM integration branch](https://github.com/jinzhen-lin/vllm/tree/bailing-v3-token-probe). Load the probe by its Hugging Face ID at server launch:

```bash
python -m sglang.launch_server \
  --model-path deepseek-ai/DeepSeek-V4-Flash-0731 \
  --probe-ckpt inclusionAI/DeepSeek-V4-Flash-0731-singprobe \
  --port 30000
```

The integrations return one score dictionary per generated token (`label_0`–`label_9`). Use the exact base-model/probe pair: `deepseek-ai/DeepSeek-V4-Flash-0731` with this checkpoint.

## Citation

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