distil-qwen3-1.7b-posthog-extractor

distil-qwen3-1.7b-posthog-extractor

A 1.7B specialist that reads a short user-behavior narration and emits product findings (bugs and UX gaps) as strict JSON. One of three tools in the distil-posthog-traffic-analyser harness; trained on the Distil Labs platform.

Task contract

Input:

Narration:
"""
<3-sentence session narration>
"""

Return JSON now.

Output — valid JSON only, matching:

{"findings":[{"kind":"bug|gap","severity":1-5,"title":"...","evidence":"..."}]}

• bug = something visibly broke or errored; gap = something the user clearly wanted to do but couldn't
• severity: 1 (cosmetic) to 5 (blocks a core flow)
• A clean session correctly yields {"findings":[]}
• No markdown, no commentary, nothing invented beyond the narration

Training

• Base model: Qwen3-1.7B (Apache 2.0)
• Teacher: openai.gpt-oss-120b (Apache 2.0)
• Seed data: 25 hand-authored, schema-validated narration/findings pairs — including 6 clean-session examples with empty findings — committed at examples/seeds/extractor.jsonl (20 train / 5 held-out test)
• Synthetic expansion: 10,078 examples generated and validated by the Distil Labs platform from the seed set
• Method: platform-managed fine-tune (task type: question-answering, JSON output)

A 0.6B variant was trained first and scored similarly on the platform judge, but live testing showed it missed low-signal findings (dead-end searches with no error keyword) entirely; the 1.7B student catches them, which is why this size ships.

Evaluation

Held-out test set, scored by the platform's LLM judge:

Live checks on this GGUF via Ollama match the hosted endpoint: 8/10 on an adversarial suite (held-out narrations plus narrations authored by the narrator SLM). The two misses are bug-vs-gap label confusion on ambiguous "silent failure" cases — the finding is still surfaced with a sensible title and severity; only the kind tag wobbles.

Head-to-head vs a frontier teacher (bun run eval, gpt-5-mini as opponent and judge, 5 demo narrations): 3 ties, 1 win for this model, 1 win for the teacher — with this model running locally at $0 per call.

Usage (Ollama)

ollama create posthog-extractor -f Modelfile   # FROM ./<this gguf>

Then in the harness .env:

TOOL_EXTRACTOR_MODEL=posthog-extractor