---
license: apache-2.0
base_model: unsloth/Qwen3-1.7B
library_name: peft
pipeline_tag: text-generation
tags: [lora, sft, structured-extraction, hardware-specs, qwen3, unsloth]
---

# FitCheck spec parser (Qwen3-1.7B LoRA)

Turns messy human descriptions of computers — "my dad's old Dell, i5, 16 gigs,
some nvidia card" — into the structured spec JSON used by
[FitCheck](https://huggingface.co/spaces/build-small-hackathon/FitCheck), the
honest "what AI can your computer run" advisor. This powers its paste box.

The rule it is trained toward: **missing information should become `null`, not a
guess.** It is tuned to prefer null over inventing, and does so far more than the
base model, but it is not perfect: on a builder-blind sealed test it still
invents a value about 18% of the time it should say null (vs 37% for the base
model). See Evaluation for the honest numbers.

## Training data: grounded, not synthetic-echo

Labels are never model-generated: every training example starts from a **real
machine** (GPUs + VRAM from a mix of vendor pages and community-compiled spec
tables, e.g. canirun.ai; 212 cards + Apple chips); only
the phrasing varies, across ~24 registers mimicking how people actually write
(casual chat, dxdiag dumps, Task Manager paste, seller listings, consoles,
comparisons, half-remembered specs, several languages). ~39% of examples have
no GPU to extract — the don't-invent cases. Trained with Unsloth (bf16 LoRA,
completion-only loss) on a single RTX 5090 laptop.

## Evaluation

### Dev set (human-written, builder-iterated, optimistic)

Evaluated on a 45-example **human-written dev set** (never generator output;
multilingual, consoles, buying-intent traps, pure refusals). The builder
iterated against this set, so these are **dev numbers**, optimistically biased
by adaptive iteration and labelled as such:

| round | field accuracy | invented-field rate (hallucination) |
|---|---|---|
| 1 | 77.3% | 32.5% |
| 3 (answer-only loss + explicit rules) | 85.8% | 12.0% |
| 5 (final) | **91.6%** | **1.2%** |

### Sealed test (builder-blind, evaluated once), the honest number

A 40-example sealed test, generated by a separate LLM that never saw the
training data and evaluated exactly once (machine-generated, so labelled as such
rather than human-written), checked for zero overlap with train and dev:

| model | field accuracy | invented-field rate |
|---|---|---|
| base Qwen3-1.7B, zero-shot | 71.5% | 37.1% |
| this LoRA | **88.0%** | **17.7%** |

The LoRA clearly beats the base model (accuracy +16.5 points, invented rate
roughly halved), but it does NOT clear the ship gate's under-5% invented-field
target on builder-blind data: the real hallucination rate is about 18%, far
above the 1.2% the adaptively-iterated dev set suggested. Reported unedited,
because catching exactly that optimism is what a sealed test is for.

Caveat: the sealed labels are machine-generated and unaudited, and some of the
"inventions" are debatable integrated-graphics cases (the model extracts an iGPU
the generator marked null), so the absolute figure carries some upward bias; a
human-audited sealed set would tighten it. The direction is unambiguous.

**Ship gate** (beat base zero-shot AND keep invented-field rate under 5%):
clears the beat-base half, fails the under-5% half on the sealed set. Treat this
as a strong extractor that nulls far more often than the base model, not a
near-zero-hallucination one. Reproduce with `scripts/eval_spec_lora.py
--testfile <sealed> --baseline <base.json>`; signed result artifacts are in the
project repo under `artifacts/`.

## Output schema

```json
{"computer": "Windows laptop|Windows desktop|Mac|Linux PC|Mini PC / Raspberry Pi|null",
 "ram_gb": "number|null", "provider": "nvidia|amd|apple|intel|none|null",
 "gpu": "string|null", "vram_gb": "number|null"}
```

Notable learned rules: `"none"` only when the text says there's no graphics
card (unknown → null); a series alone ("gtx") is a provider, not a GPU; a
stated VRAM figure beats the model's knowledge of that card; dxdiag's
"Display Memory" is not system RAM; "8gb dev kit" on a Jetson is unified RAM,
not VRAM; two machines compared → extract nothing.

Part of the FitCheck project (Build Small hackathon): a deterministic engine
does the math; small models appear only where they earn their place.