OSINT Argus — Gemma 4 12B QAT, unified news + social OSINT (W4A16, tri-modal)
All numbers on this card are measured on the deployed int4 artifact (previous cards
quoted bf16-checkpoint numbers; the int4 reality is ~10pp lower on schema tasks — that
discrepancy was the programme's audit finding A1, now fixed).
Unified build (2026-09): full-parameter QAT on the unified trunk — no adapter, no merge.
Lineage: social-media soup (25-Aug) -> news track v20/v21 (120,747-record corpus: 40% news
core — article veracity in Bengali/Hindi/Urdu/25-language x-fact + Reddit-format Fakeddit,
FEVER-family claim adjudication, XL-Sum summaries in 9 languages, article political bias —
plus full social/multimodal replay). Exported through the corrected int4 grid (earlier
revisions shipped fq(fq(W)); fixed 31-Aug, verified lossless: baked-checkpoint == export).
Selected over v20/v22 and the deployed soup-int4 on combined social/multimodal + news gates.
Text gates — 200 items/task, bare decoding, deployed int4
| metric | stock Gemma-4 QAT (bf16) | this model (int4) |
|---|
| schema_lock (11-key OSINT record) | 0.0 | 0.875 |
| NER micro-F1 | 0.6163 | 0.7 |
| sentiment acc | 0.76 | 0.775 |
| claim refuted-F1 | 0.8125 | 0.88 |
| halluc-detect F1 | 0.3981 | 0.4786 |
| grounded-answer schema | 0.9536 | 1.0 |
| general MCQ % | 59.0 | 52.5 |
NLI-judged faithfulness (mDeBERTa-v3 multilingual, P>=0.70, all-sentences-entailed): 0.059602649006622516
(strict sentence-level entailment of abstractive answers — reported for transparency;
schema-validity above measures format compliance only)
News gates — held out by content-hash firewall, never trained on (new in this build)
| metric | this model |
|---|
| news veracity — misinfo accuracy | 0.2335 |
| news claim — macro F1 / acc | 0.3956 / 0.4643 |
| news bias — accuracy | 0.3667 |
| news summary — schema / filled | 0.425 / 0.475 |
Multimodal gates — int4
| metric | this model |
|---|
| vision OCR — CER ↓ | 0.6368 |
| vision misinformation — accuracy | 0.525 (majority baseline 0.5083) |
| vision hostility — accuracy | 0.4434 (majority baseline 0.6321) |
| audio ASR — CER ↓ | 0.6835 |
| audio language-ID — accuracy | 0.1833 (majority baseline 0.175) |
| audio abusive-speech — accuracy | 0.4 (majority baseline 0.5417) |
Serve
1python -m sglang.launch_server --model-path <this-repo> \
2 --quantization compressed-tensors --mem-fraction-static 0.45 --context-length 8192
Training code, gate reports, gap audit, hold/ship decisions:
shubro18202758/osint-argus on GitHub.
Card generated 2026-09-03 from this artifact's own int4 gate reports.
