165 lines
6.4 KiB
Markdown
165 lines
6.4 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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tags:
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- caid
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- blueprint
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- hardware
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- cad
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- text-to-cad
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- manufacturing
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- agents
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- structured-generation
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- json
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- qwen2.5
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- unsloth
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pipeline_tag: text-generation
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base_model: Qwen/Qwen2.5-3B-Instruct
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library_name: transformers
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---
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# Blueprint Base — Qwen2.5-3B
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**Blueprint turns a plain-English hardware idea into an organized project plan.**
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Tell it what you want to build — *"a compact desk clock with an e-ink display and a remote"* —
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and it gives back a structured blueprint: the parts list, how the parts connect, step-by-step
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build instructions, rough costs, and a quick design check. Everything comes out as clean,
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organized data that an app can read and build on.
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This is the **all-in-one model** — it runs on its own, no add-ons needed. (There's also a small
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adapter-only version at
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[**blueprint-base-lora**](https://huggingface.co/caid-technologies/blueprint-base-lora).)
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> **Early research preview.** Great for drafting and exploring ideas — not a replacement for
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> real engineering, CAD software, or safety review.
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By [caid-technologies](https://huggingface.co/caid-technologies).
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---
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## What it can do
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Give it a hardware idea and it can produce any of:
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- 📋 a **parts list** (components)
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- 🔌 a **wiring/connection map** between the parts
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- 🛠️ ordered **build steps**
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- 💲 rough **sourcing and cost** info
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- ✅ a basic **design check**
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- 📦 or the **whole project plan** at once
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You can ask for the complete plan, or just one piece (like only the parts list).
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## What it's good for — and not
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✅ **Good for:** brainstorming hardware projects, drafting parts lists and build steps, and
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turning a rough idea into an organized starting plan.
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🚫 **Not for:** final engineering decisions, real CAD models, electrical safety, or anything
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safety-critical. Treat the output as a helpful **first draft to review**, not a finished design.
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## Try it
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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REPO = "caid-technologies/blueprint-base"
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model = AutoModelForCausalLM.from_pretrained(REPO, device_map="auto", torch_dtype="bfloat16")
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tok = AutoTokenizer.from_pretrained(REPO)
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msgs = [
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{"role": "system", "content":
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"You design hobbyist electronics projects. Given a request, reply with a single "
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"JSON object describing the full project. Output only the JSON."},
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{"role": "user", "content": "A compact desk clock with an e-ink display and an IR remote."},
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]
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inputs = tok.apply_chat_template(
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msgs, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)
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out = model.generate(**inputs, max_new_tokens=6144, do_sample=False,
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repetition_penalty=1.1, pad_token_id=tok.eos_token_id)
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print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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💡 **Tip:** keep `do_sample=False` (greedy decoding — sampling degrades the JSON output),
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keep `max_new_tokens` high (≥ 6000) so long plans aren't cut off, and keep
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`repetition_penalty=1.1` so wiring lists don't get stuck repeating. For Ollama/local apps,
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convert this model to GGUF with llama.cpp.
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## What it learned from
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It was trained on about **130 hobbyist hardware projects** — things like weather stations,
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small robots, drones, smart-home gadgets, lab tools, and audio gear — expanded into a few
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thousand practice examples. Everything is **small, maker-style** electronics-plus-hardware.
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**Most common project types in the training data:**
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| Project type | Share | Examples |
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|---|---|---|
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| Test & lab instruments | ~20% | function generator, Geiger counter |
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| Smart-home / IoT gadgets | ~15% | pet feeder, smart mailbox, pill dispenser |
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| Radio, comms & networking | ~9% | LoRa base station, APRS tracker, NAS |
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| Wearables & health | ~8% | sleep ring, heart-rate strap |
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| Audio & music | ~8% | synth module, guitar pedal, speaker |
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| Robotics & motion | ~7% | quadruped robot, robotic arm |
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| Environmental sensing | ~7% | air-quality monitor, weather station |
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| Clocks & e-ink displays | ~6% | word clock, e-ink calendar |
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| Maker / fabrication tools | ~5% | vinyl cutter, pen plotter |
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| Drones & aerial | ~5% | FPV drone, VTOL aircraft |
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| Everything else | ~10% | lighting, games, automotive, power |
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## Good to know (limitations)
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- It's a **small model**, so complex, many-part projects are harder for it.
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- It **proposes** designs; it doesn't verify them. Always sanity-check before building.
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- It's strongest on common project types (lab tools, smart-home) and weaker on rarer ones
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(games, automotive).
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## How well it works
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We tested it on projects it had **never seen during training**. Here's how often it produced a
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valid, well-structured result for each task:
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| Task | Valid result |
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|---|---|
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| 🛠️ Build steps | ~100% |
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| ✅ Design check | ~100% |
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| 📋 Parts list | ~95% |
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| 📦 Full project plan | ~85–97% |
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| 🔌 Wiring map | ~67% |
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It's strongest at build steps, design checks, and parts lists. Full end-to-end plans are close
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behind, and wiring maps are the hardest (and most sensitive to the `repetition_penalty` tip
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above). *Figures are from held-out testing and are being finalized for the current version.*
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---
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<details>
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<summary><b>Technical details</b> (for ML folks)</summary>
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- **Base model:** `Qwen/Qwen2.5-3B-Instruct`; this repo is the **fine-tune merged to 16-bit**
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(standalone, no adapter needed).
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- **Method:** QLoRA with Unsloth (LoRA r=32, alpha=32, all attention+MLP projections), then merged.
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- **Training:** 1 epoch, max_seq_len 6144, effective batch 8, lr 2e-4 (linear, 3% warmup),
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adamw_8bit, NEFTune α=5, loss masked to assistant turns, early stopping on eval loss
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- **Hardware:** single RTX 4070 (12 GB)
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- **Data:** synthetic dataset projected into 6 task "modes" (full plan, parts, wiring,
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instructions, validation); split **grouped by project** so none leak between train/test.
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~3,242 rows; modes rebalanced (cap 350/mode) so the model doesn't coast on the easy ones.
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- **Inference:** `do_sample=False`, `repetition_penalty≈1.1`, `max_new_tokens≥6000`, pass the
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attention mask.
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```bibtex
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@misc{blueprint_base,
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title = {Blueprint Base: Qwen2.5-3B for structured hardware project generation},
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author = {Caid Technologies},
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year = {2026},
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howpublished = {\url{https://huggingface.co/caid-technologies}}
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}
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```
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Built with [Unsloth](https://github.com/unslothai/unsloth) and 🤗 Transformers / PEFT / TRL.
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</details>
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