---
license: apache-2.0
base_model: andreasmartin/apertus-v1.5-8b-text
library_name: peft
language: [de, fr, it, en]
tags: [apertus, lora, agent-audit, tool-calling, hack-apertus]
---

# rosetta-flow auditor adapters (Apertus 8B)

LoRA adapters for **rosetta-flow**, an open, on-premise auditor for AI-agent runs built with Apertus for
Hack Apertus 2026 (Track 2B). The auditor reads an agent run like a code repository (each step is a file), investigates
it with tools (`read_spans`, `search`, `list_spans`) and ends with a typed `report` whose evidence quotes are checked
word for word against the cited steps.

These adapters are an **honest experiment**: fine-tuning made the 8B more reliable and faster, not more accurate. The
shipped system uses the untrained 8B with the harness as screener and Apertus 70B as confirmer. Details, data and code
are in the rosetta-flow repository and technical report.

## Adapters

| Folder | What | Training |
|---|---|---|
| `full-2ep/` | v1: SFT on 70B investigations | 2 epochs, 4-bit QLoRA, bf16 compute, L4, 92 min |
| `full-3ep-bf16/` | **v2**: same data, unquantised | 3 epochs, bf16, batch 4 x accum 2, A100, 38 min |
| `full-3ep-bf16-analysis/` | v3: v2 plus the 70B's rule-by-rule analysis before each report | 3 epochs, bf16, A100, 47 min |
| `rl-screener-v1/` | GRPO single-pass screener (ok / issues + probability) | 80 steps, 8 generations, lr 2e-5, 85 min |

All: base `andreasmartin/apertus-v1.5-8b-text` (text-only Apertus v1.5 8B), LoRA rank 16, alpha 32, dropout 0.05, all
linear projections, lr 1e-4 (SFT), max length 8,192, seed 0, one run per configuration. Each folder has `train_log.json`
with the arguments and loss history.

**Data:** 414 balanced examples (207 with an injected error, 207 clean; refunds 240, manufacturing 174), kept by
rejection sampling from Apertus 70B investigations of simulated agent runs (557 of 829 accepted). Loss only on the
auditor's turns (~9 % of tokens; 23 % for v3).

## Results (held-out test, 262 runs, balanced accuracy)

| System | Refunds | Manufacturing | Failed audits |
|---|---|---|---|
| B1 untrained 8B + harness | 0.62 | 0.54 | 20 |
| B2 v2 adapter + harness | 0.64 | 0.52 | 1 |
| B3 Apertus 70B + harness | 0.69 | 0.71 | 0 |

v2: failed audits 20 -> 1, verified evidence 0.60 -> 0.78, 2-3x faster, but more false alarms (0.39 -> 0.51). v3 on dev
was too cautious (0.56 / 0.48). The GRPO screener did not beat the untrained 8B on dev (0.53 / 0.48 vs 0.54 / 0.48) and
its probabilities were not calibrated. All under a small budget (one seed, no search); not proof that training cannot
help.

## Use

```bash
vllm serve andreasmartin/apertus-v1.5-8b-text --enable-lora --max-lora-rank 16 \
  --lora-modules auditor=<local path>/full-3ep-bf16/adapter \
  --chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus
```

Then point rosetta-flow at it with `LLM_BASE_URL=http://localhost:8000/v1` and `LLM_NAME=auditor`.

## Limits

Trained only on simulated runs in two domains (refunds, manufacturing quality) and four languages. Not validated on
real production traces. Use as a research artefact, with a human reviewing findings.

## Licence

Apache-2.0, like Apertus. Base model: swiss-ai Apertus v1.5 (Apache-2.0), text-only conversion by FHNW.