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