slotloss

Per-grammar-role loss analysis for structured JSON output from language models.

Fine-tuning your LLM for JSON? Your aggregate metrics might be hiding per-field regressions.

pip install slotloss

What it does

slotloss is the observability layer for structured JSON output. It meets you wherever you are:

The problem

Standard fine-tuning + grammar-constrained decoding produces valid JSON. Aggregate loss improves. But:

STRUCTURAL         5.33 -> 0.00     -100%   OK
KEY                0.47 -> 0.00     -100%   OK
BOOLEAN            0.46 -> 1.05     +130%   !! REGRESSION
TOTAL              0.55 -> 0.17      -69%

Aggregate loss improved 69%. Boolean prediction got 130% worse. slotloss catches this.

Quick start

See QUICK_START.md for a hands-on walkthrough from messy output to full analysis.

Python API

from slotloss import analyze

report = analyze(
    model_name="Qwen/Qwen2.5-0.5B-Instruct",
    checkpoint="my_lora/",
    schema="schema.json",
    data="test.jsonl",
)
print(report)

if report.regressions:
    print(f"REGRESSIONS: {[r.role for r in report.regressions]}")

Exit code is 1 if regressions are detected. Use in CI/CD.

Grammar Roles

Links

• Quick Start Tutorial
• Fixing JSON Fine-Tuning — mitigations, services
• Paper — "Valid JSON, Wrong Answer" (2026)

License

MIT