proofkit-distilled-qwen0.5b-gguf

ProofKit Qwen 0.5B — distilled (GGUF)

The llama.cpp / GGUF build of visproj/proofkit-distilled-qwen0.5b — a Qwen 0.5B student distilled from ProofKit's fine-tuned gpt-oss-20b teacher. This is the default model the ProofKit Space serves: it runs free on CPU via llama.cpp, so the app works on a free Space with no GPU.

• Quantization: q4_k_m (~400 MB)
• Runtime: llama-cpp-python / llama.cpp
• Chat template: Qwen2 (embedded in the GGUF metadata)

Usage

from llama_cpp import Llama
llm = Llama.from_pretrained(
    repo_id="visproj/proofkit-distilled-qwen0.5b-gguf",
    filename="*q4_k_m.gguf",
    n_ctx=4096,
)
resp = llm.create_chat_completion(
    messages=[{"role": "system", "content": SYSTEM}, {"role": "user", "content": PROMPT}],
    temperature=0.0,
)
print(resp["choices"][0]["message"]["content"])

Configure it in ProofKit with:

export PROOFKIT_DISTILLED_MODELS='ProofKit Qwen 0.5B Distilled=visproj/proofkit-distilled-qwen0.5b-gguf|*q4_k_m.gguf'

Evaluation (post-fix, 3-judge panel)

Mean score (0–100) on 15 held-out prompts, graded by Claude Opus 4.7, GPT-5.5, and a local Qwen-3B (gpt-oss experts is a deliberately un-retrained stale control):

Both served retrained 0.5Bs beat the stale control and every untuned base across all three judges, and the distilled 0.5B ≈ ties its own 20B teacher.

About ProofKit

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

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.