Copy from visproj for Build Small Hackathon submission

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README.md

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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.

proofkit_sft.jsonl

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