hqt2yotoz/verdict-engine-triage-v2

Verdict Engine — SFT v2 (Sonnet-4.6 distilled)

The supervised fine-tuning dataset behind every model in the Verdict Engine bake-off: four LoRA fine-tunes (hqt2yotoz/verdict-engine-{qwen3-4b-2507,qwen3-8b,smollm3-3b,qwen2.5-7b}-lora) that all beat their own base on a blind panel. This is the v2 / "true-distillation" dataset — the one whose labels come from a genuinely stronger teacher (Claude Sonnet 4.6), not from the small model labeling itself.

What it teaches

The Verdict Engine has two faces, and this dataset trains both with one chat schema family (messages: system / user / assistant, where the assistant turn is a single JSON object):

Task	Examples (train / val)	Persona(s)	Assistant JSON
Triage	92 / 12	The Librarian	label ∈ {read-now, skim, skip, archive}, novelty_score ∈ [0,1], one_line, tags (2–5), reason
Critic panel	376 / 40	Reviewer #2 · The Over-Eager Intern · The Weary Professor · The Hype Beast	verdict, roast, score
Total	468 / 52

Triage label distribution (train split)

skip 58 · read-now 16 · skim 14 · archive 4.

This is deliberately skip-heavy, and that is the point. Most of the daily model/dataset/paper firehose is genuinely skippable; a calibrated triager should say so. The single most important finding behind these models is that base models over-praise (almost everything → read-now with inflated novelty), while a model distilled on this distribution learns to discriminate — which is what wins the blind eval. A balanced label set would have taught the wrong thing.

Provenance — true distillation, not self-distillation

Items are real entries from the Hugging Face firehose (newest models & datasets via huggingface_hub.list_models / list_datasets, plus the HF Daily Papers API) and a small set of hand-seeded items. Each item's gold label was produced by Claude Sonnet 4.6 through an AI gateway acting as the teacher (finetune/gateway_teacher.py), then formatted into chat SFT records (finetune/gen_dataset.py).

This is the methodological core of the project. An earlier v1 dataset was labeled mostly by the same 7B model being fine-tuned — self-distillation — and the resulting fine-tune lost its blind eval 6/3/3 (a model can't teach itself a skill it lacks). Re-labeling with a true stronger teacher (this v2 set) flipped that to a 12–0 sweep on the original 7B, and to clean wins for all four bases in the multi-model bake-off. See docs/TRAINING.md.

Files

• train_v2.jsonl — 468 records, one JSON object per line, key messages.
• eval_v2.jsonl — 52 held-out records, same schema.

The held-out bake-off eval items (the fixed 10 the blind panel scored every model on) and the blind/keyed eval pairs live in the GitHub repo under finetune/bakeoff/.

How it was used

Identical recipe across all bake-off models — only the base varies: LoRA r=16, α=16, targets q,k,v,o,gate,up,down; bf16 LoRA via PEFT + TRL (not 4-bit — the Unsloth 4-bit path corrupted adapter weights for these bases; see TRAINING.md §4); trained on each model's native chat template; 3 epochs on an AWS A10G. Reproduce:

python finetune/train_lora_mps.py \
  --model Qwen/Qwen3-4B-Instruct-2507 \
  --train train_v2.jsonl --eval eval_v2.jsonl \
  --out out_qwen3 --epochs 3

Honest limitations

• Synthetic labels. Gold verdicts are Sonnet-4.6 judgments, so the dataset inherits that teacher's taste and blind spots. It is a narrow, in-domain classifier signal — good for this one triage/critique schema, not a general-purpose judge (cf. Huang et al., ACL 2025 Findings, "A Fine-tuned Judge Model is not a General Substitute for GPT-4").
• Small + imbalanced. 520 examples, skip-heavy triage labels, critic-mode dominant. Treat as a task-shaping SFT set, not a benchmark.
• Item text is public metadata (HF card snippets, arXiv-style abstracts, titles) captured in June 2026; original works retain their own licenses. The labels/personas are model-generated. license: other reflects this mixed provenance — released for research/reproducibility of the Verdict Engine experiment.

— Built for the Hugging Face Build Small hackathon. Code: github.com/setuc/verdict-engine