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