---
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: transformers
pipeline_tag: text-generation
language:
- en
tags:
- proofkit
- fine-tuned
- sft
- lora
- build-small-hackathon
- work-sample
datasets:
- visproj/proofkit-sft
---

# ProofKit Qwen 0.5B — fine-tuned (direct SFT)

`Qwen/Qwen2.5-0.5B-Instruct` fine-tuned **directly** on the ProofKit SFT set
([`visproj/proofkit-sft`](https://huggingface.co/datasets/visproj/proofkit-sft),
~7,000 synthetic examples). LoRA-trained, then merged to standalone weights. This is
the **in-Space Transformers** option in [ProofKit](https://huggingface.co/spaces/visproj/proofkit) (runs on ZeroGPU / a small
GPU; loads lazily on first generation).

It learns ProofKit's task contracts — section drafting, co-author drafting from rough
user answers, revision actions, and strict-JSON scenario / recommendation / readiness
/ portfolio generation.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

m = "visproj/proofkit-qwen0.5b-7k"
tok = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")  # carries the chat template
model = AutoModelForCausalLM.from_pretrained(m, torch_dtype=torch.float16).to("cuda").eval()

# NOTE: prompt it with ProofKit's trained prompt_formats.py shapes — see below.
messages = [{"role": "system", "content": SYSTEM}, {"role": "user", "content": PROMPT}]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
ids = tok(text, return_tensors="pt").to("cuda")
out = model.generate(**ids, max_new_tokens=600, do_sample=True, temperature=0.3, top_p=0.9)
print(tok.decode(out[0][ids["input_ids"].shape[-1]:], skip_special_tokens=True))
```

## 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):

| model | Claude | GPT-5.5 | Qwen-3B | **Avg** |
|---|---:|---:|---:|---:|
| gpt-5.5 (frontier ceiling) | 94.6 | 95.6 | 90.8 | **93.7** |
| gpt-oss attn (retrained teacher) | 82.0 | 66.8 | 81.4 | **76.7** |
| **qwen-0.5b distilled (served)** | 79.0 | 68.6 | 82.2 | **76.6** |
| **qwen-0.5b direct 7k (served)** | 78.6 | 64.4 | 82.0 | **75.0** |
| gpt-oss experts *(stale control)* | 67.6 | 68.6 | 81.8 | **72.7** |
| qwen-3b base | 62.1 | 67.1 | 80.5 | **69.9** |
| gpt-oss base | 55.4 | 53.8 | 68.2 | **59.1** |
| qwen-0.5b base | 36.5 | 44.5 | 67.9 | **49.7** |

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.

## Limitations

- **0.5B capacity.** Reliably carries trained-style specifics, but can miss a truly
  arbitrary novel token and occasionally garbles. ProofKit adds a runtime fallback to a
  hosted instruct baseline when a draft drops the user's answers.
- **Prompt-format-frozen** (see below) — not a general chat model.

## 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.