Instructions to use cn0303/fitcheck-spec-parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cn0303/fitcheck-spec-parser with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "cn0303/fitcheck-spec-parser") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use cn0303/fitcheck-spec-parser with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cn0303/fitcheck-spec-parser to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cn0303/fitcheck-spec-parser to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cn0303/fitcheck-spec-parser to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="cn0303/fitcheck-spec-parser", max_seq_length=2048, )
FitCheck spec parser (Qwen3-1.7B LoRA)
Turns messy human descriptions of computers β "my dad's old Dell, i5, 16 gigs, some nvidia card" β into the structured spec JSON used by FitCheck, the honest "what AI can your computer run" advisor. This powers its paste box.
The one rule that matters: missing information becomes null, never a
guess. The model is trained and gated specifically against inventing specs.
Training data: grounded, not synthetic-echo
Labels are never model-generated: every training example starts from a real machine (GPUs + VRAM from vendor spec tables, 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 β the don't-invent cases. Trained with Unsloth (bf16 LoRA, completion-only loss) on a single RTX 5090 laptop.
Evaluation: human-written text only
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 β 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% |
A sealed test set (written by people who never saw the training data, evaluated exactly once, builder-blind) is pending; its result will be added here unedited when run. Ship gate: beat the base model zero-shot AND keep the invented-field rate under 5% β passed on dev.
Output schema
{"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 β 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 β 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.
- Downloads last month
- 10