---
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
- prompt-injection
---

# SecJev-4B-Joint-Redo

Redo fine-tune of SecJev-4B for Joint Spot 1 + Spot 2 (single checkpoint, two questions). This repository root is a complete
Kev checkpoint directory (LoRA adapter + trained head + tokenizer), loadable
with the Kev loader. The Qwen3.5-4B-Base backbone is not re-hosted;
`bootstrap.py` fetches the pinned backbone and Kev source.

## Training

- Compute: DigitalOcean GPU droplet `jev-train-1` (NVIDIA H100 80GB), 2026-11-03 23:03:28–23:37:49 UTC.
- Method: `ft_action.py`, 1 epoch, lr 3e-5, batch 32 questions, token budget
  4096, gradient checkpointing, fp32 weights with bf16 autocast.
  Trainable parameters: 33,776,128 (LoRA + head); backbone frozen.
- Data: `data/bench/train_joint_t.jsonl` (train) / `data/bench/dev_joint_t.jsonl` (dev), jev-stageA redo bench sets.
- Full training log: `redo-training-log.jsonl` and `redo-training.stdout.txt`
  in this repository. Machine-readable details: `redo-provenance.json`.

## Dev results (only evaluation so far)

Corrected per-question re-evaluation of this checkpoint on the dev file
(score: authorization = P("2") + 0.5·P("1"); injection = P("true")):

| question | dev n | dev pos | AUC | TPR@0.5 | FPR@0.5 |
|---|---|---|---|---|---|
| authorization | 500 | 85 | 0.9898 | 0.9412 | 0.0048 |
| injection | 500 | 250 | 0.9314 | 0.7760 | 0.0200 |

No frozen-test, adaptive-attack, or independent-replication result exists yet
for this checkpoint. Dev rows were used for training-time monitoring. Treat
the numbers above as development measurements, not deployment guarantees.

Note: the training-time dev metrics printed in `redo-training-log.jsonl` for
injection-question runs used an authorization-only scoring formula and are
invalid for those runs (they show AUC 0.0 / 0.14); the table above supersedes
them. Training itself used correct targets.