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license: apache-2.0
language:
- en
tags:
- proofkit
- sft
- synthetic
- build-small-hackathon
task_categories:
- text-generation
size_categories:
- 1K<n<10K
---
# ProofKit SFT dataset
The supervised fine-tuning set for ProofKit's small models (~7,000 chat examples).
**Fully synthetic and license-safe** — examples are generated deterministically from
ProofKit's own templates, demo profiles, and role-knowledge records
(`data/finetune/build_dataset.py`). No scraped prose, no private user data, no
model-generated targets.
## Tasks
`section_draft`, `coauthor_draft` (draft from rough user answers), `section_revision`,
and strict-JSON `scenario_json` / `recommendation_json` / `review_json` /
`portfolio_json` / `custom_outline_json`. Each row is a `messages` list (system / user /
assistant).
## Design: faithfulness + variation
This set was rebuilt to fix a **synthetic-data leakage** bug. The earlier version
rendered the example *user answers* and the *target* from the same skill/constraint
slots, so models learned to ignore inputs and reproduce a template. The fix:
- **Faithfulness anchors** — each synthetic answer carries a distinctive token (e.g.
*"the vendor scorecard"*) the target must preserve, teaching `target = f(input)`.
- **Seeded per-example variation** — every task draws phrasing from a per-example
deterministic RNG, so the data stops drilling the same few sentence frames (the
readiness review alone went from ~4 canned reasoning strings to 86 distinct).
## Used by
[`visproj/proofkit-qwen0.5b-7k`](https://huggingface.co/visproj/proofkit-qwen0.5b-7k)
(direct SFT) and [`visproj/proofkit-gpt-oss-20b-lora`](https://huggingface.co/visproj/proofkit-gpt-oss-20b-lora)
(teacher).
## About ProofKit
[ProofKit](https://huggingface.co/spaces/visproj/proofkit) is a work-sample generator for job seekers — it turns a target
role, background, and skills-to-prove into a realistic, **clearly-fictional**
practice work sample (a role-specific challenge, a guided builder, a readiness
review, and a recruiter-ready portfolio packet). Built for the Hugging Face **Build
Small Hackathon** (Backyard AI track). Integrity rules are load-bearing: outputs
never claim real employment, metrics are labeled hypothetical, and exports carry an
ethical disclosure.
### The ProofKit model family
| Repo | What it is |
|---|---|
| [`visproj/proofkit-qwen0.5b-7k`](https://huggingface.co/visproj/proofkit-qwen0.5b-7k) | Qwen2.5-0.5B fine-tuned directly on the 7k set (Transformers) |
| [`visproj/proofkit-gpt-oss-20b-lora`](https://huggingface.co/visproj/proofkit-gpt-oss-20b-lora) | gpt-oss-20b LoRA — the distillation **teacher** |
| [`visproj/proofkit-distilled-qwen0.5b`](https://huggingface.co/visproj/proofkit-distilled-qwen0.5b) | Qwen2.5-0.5B distilled from the teacher (merged) |
| [`visproj/proofkit-distilled-qwen0.5b-gguf`](https://huggingface.co/visproj/proofkit-distilled-qwen0.5b-gguf) | GGUF of the distilled student (llama.cpp — **served**) |
| [`visproj/proofkit-sft`](https://huggingface.co/datasets/visproj/proofkit-sft) | SFT dataset (synthetic, license-safe) |
| [`visproj/proofkit-distill-qwen0.5b`](https://huggingface.co/datasets/visproj/proofkit-distill-qwen0.5b) | Distillation dataset (teacher completions) |
### A note on training data (the "static responses" fix)
An earlier version of these models produced repetitive, input-ignoring drafts. The
root cause was **synthetic-data leakage**: the dataset rendered the example *user
answers* and the *target* from the same template slots, so the model learned
`target = template` instead of `target = f(input)`. The fix — **faithfulness anchors**
(a distinctive token shared by the answer and the target) + **seeded per-example
variation** across every task, then a full-chain retrain — is what these current
weights reflect.
### Prompt format is a frozen contract
These 0.5B models were trained on the **exact** prompt shapes from ProofKit's
`prompt_formats.py`. They only behave well when prompted in that format; reworded or
free-form prompts push them off-distribution. They are purpose-built components of the
ProofKit app, not general chat models.
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