Turns a hardware idea — a sentence, a short brief, even a sketch — into a complete build blueprint.
Tell it what to build — "a USB-powered desk lamp with touch dimming" — optionally with a short
document or a concept image, and it returns one structured blueprint plan: the parts, the
wiring, ordered build steps, a costed sourcing table, and an appearance spec with a ready-to-use
image-generation prompt. It's a standalone, all-in-one model (no adapter upon request).
Early research preview. For drafting and exploring ideas — not a replacement for real
engineering, CAD, or safety review.
What it does
Give it a hardware idea and it returns, as one machine-readable JSON object:
📋 a parts list (electronics, printed parts, fasteners, with dimensions)
🔌 the wiring between parts, with a power budget and basic protection
🎨 an appearance spec plus a ready-to-use image-generation prompt
Your app can parse, check, and build on the result directly.
Results
We test on requests the model has never seen during training. How often it produces a valid,
well-structured blueprint:
On held-out requests
Stock Qwen3.5-9B
Parti-Vision (free)
Parti-Vision (guided)
Valid, well-structured blueprint
FAIL
67%
83%
On brand-new realistic requests, guided decoding reaches 97% valid blueprints (61% with
free decoding); the stock base model manages FAIL on the same tests.
What's "guided decoding"? A standard serving option (guided_json in vLLM, "structured
outputs" in most hosted APIs) that constrains the output to your blueprint format. The model still
makes every design decision — the parts, the wiring, the steps, the costs — guided decoding just
guarantees the shape.
What's improved
FAIL → PASS The stock base model can't produce a valid blueprint; Parti-Vision does —
67% free, 83% guided, and 97% on realistic requests with guided decoding.
Reads sketches, renders, and short briefs — not just text. Prompt + brief + render is
strongest.
General image understanding preserved — the visual pathway is left intact during fine-tuning.
Answers in JSON directly — no reasoning preamble to strip.
Cleaner than earlier iterations — no leaked reasoning, no markdown fences, no leading prose
(all regression-checked).
What you can give it
A plain-English request — one or two sentences.
A short document — a brief or notes, pasted into the message.
A concept image — a hand-drawn sketch or product render, as a vision input.
Any combination works; prompt + brief + render is strongest. The model reads text + images, so
convert PDFs, LaTeX, CAD files, or spreadsheets to text or an image first.
Try it
The model answers in JSON directly — no reasoning preamble to strip.
python
1from unsloth import FastVisionModel
23REPO ="caid-technologies/parti-vision"4model, tok = FastVisionModel.from_pretrained(REPO, load_in_4bit=False)5FastVisionModel.for_inference(model)67SYSTEM_PROMPT =(8"You design maker/electronics products. Given a request, reply with one JSON object "9"describing the complete build — parts, wiring, build steps, sourcing, and appearance. "10"Output only the JSON."11)12messages =[13{"role":"system","content":[{"type":"text","text": SYSTEM_PROMPT}]},14{"role":"user","content":[{"type":"text","text":"Design a USB desk lamp with touch dimming."}]},15]16text = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)17inputs = tok(text=text, return_tensors="pt").to("cuda")18out = model.generate(**inputs, max_new_tokens=13000, do_sample=False, repetition_penalty=1.1)19print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
💡 Blueprints are long: keep max_new_tokens high and the repetition penalty on. To include an
image, add {"type": "image", "image": your_image} to the user content and pass images= to the
tokenizer.
🚀 Serving it for real? Use vLLM with guided_json (or your engine's structured-outputs mode)
constrained to your blueprint format — it takes valid-blueprint rates on unseen prompts from ~61%
to ~97%.
Good to know
English prompts, maker/electronics domain. Off-topic requests still get a blueprint, not a refusal.
Outputs are drafts, not verified engineering — roughly half have at least one design slip (a wiring
mistake, costs slightly off, a misordered step). Re-validate in your app, and review before you solder.
Very long plans can get cut off at the token cap; contradictory or impossible requests can produce
confidently wrong blueprints.
Learn more
📄 Technical whitepaper (PDF) —
the model family, training approach, evaluation methodology, and the complete honest scorecard.
