| --- |
| language: |
| - en |
| license: other |
| license_name: secjev-use-and-distribution-agreement-v1.0 |
| license_link: LICENSE |
| base_model: Qwen/Qwen3.5-4B-Base |
| tags: |
| - security |
| - decision-model |
| - lora |
| --- |
| |
| # SecJev-4B |
|
|
| > **Mirror note (macmacmacmac):** This repository is an unmodified mirror of the upstream SecJev-4B v1.2.0 |
| > release from [UESTC1010/SecJev](https://github.com/UESTC1010/SecJev/releases/tag/v1.2.0), redistributed |
| > under section 3 of the SecJev Use and Distribution Agreement v1.0 with `LICENSE`, notices, and provenance |
| > retained. The mirror exists so the [jev-stageA](https://github.com/maceip/jev-stagea) JEV agent-defense |
| > evaluations can fetch both required checkpoints (this checkpoint and |
| > [macmacmacmac/SecJev-4B-Action-Monitor-C1](https://huggingface.co/macmacmacmac/SecJev-4B-Action-Monitor-C1)) |
| > from the Hub. Cite the upstream SecJev project for the checkpoint. The original upstream model card |
| > follows unchanged. |
|
|
| SecJev-4B scores Boolean judgments, choices and ordered ratings from security evidence and explicit policies. It returns probabilities over candidate answers instead of generating answer text. |
|
|
| This release contains the **trained LoRA adapter, decision head, tokenizer, calibration and inference code**. Download the pinned Qwen3.5-4B-Base backbone separately with the included bootstrap. We adapted official Kev-4B on SecJev-Corpus for three epochs; full development selected epoch 3. Temperature 1.3349681034 is fitted on calibration only. |
|
|
| ## Run |
|
|
| ```bash |
| python -m pip install -r SecJev-4B/requirements.txt |
| export SECJEV_WORK="$PWD/secjev-4b-work" |
| python SecJev-4B/bootstrap.py --inference-only |
| python SecJev-4B/infer.py --model ./SecJev-4B --base "$SECJEV_WORK/base" --request SecJev-4B/examples/certificate.json --device cuda |
| ``` |
|
|
| The included example predicts `reject`; the actual answer is in example-output.json. Six short development cases (two per type) confirm export parity. They are a packaging check, not an accuracy estimate. |
|
|
| ## Data and results |
|
|
| SecJev-Corpus v1.0.0 has 170,185 questions from eight sources and 14 tasks, split into 118,818 training / 14,821 calibration / 14,912 development / 21,634 test questions. [Download the unchanged corpus](https://github.com/UESTC1010/SecJev/releases/download/v1.0.0/SecJev-Corpus-v1.0.0.tar.gz). |
|
|
| On all security test questions: **88.01% micro accuracy, 94.51% macro-task accuracy, 92.42% macro-task balanced accuracy**. Original Kev clean suites: 86.58% decision-v7 and 83.84% transfer-v4. [Full per-task results and all three checkpoints](evaluation/README.md). Vehicle tasks remain weaker; research splits were inspected during corpus construction and are not a new blind deployment benchmark. |
|
|
| ## Training and terms |
|
|
| Nine RTX 3090 GPUs, three epochs, LoRA r16/alpha32 across attention/MLP/DeltaNet, 256-dimensional head, FP32 masters and BF16 autocast. [Reproduction code](https://github.com/UESTC1010/SecJev/tree/main/training/secjev4b). Exact initialization and selection records are included in provenance.json and security-training-config.json. |
|
|
| The SecJev Use and Distribution Agreement v1.0 and applicable upstream source/model terms apply. See LICENSE, SOURCES.md and THIRD_PARTY_NOTICES.md. This release does not replace the project agreement with Apache-2.0. |
|
|
