townbox/lowpoly-coder-4b-data

LowPoly Coder 4B training data

This is the complete training data of townbox/lowpoly-coder-4b, and nothing else. Each row pairs a one-sentence request for a 3D object ("Build a low-poly accent cabinet.") with the answer the model was trained to give: a short spec, then JavaScript that builds the object as a low-poly Three.js (r180) asset.

Contents

Config	What it is	train	validation
stage1 (default)	Answers written by DeepSeek V4.1 Flash, kept only when the code ran and passed technical checks	7,599	412
stage2	Answers written by the stage-1 model itself, kept only when they passed all technical checks and a vision model judged the render recognisable	4,245	199

• Training order: the model was fine-tuned from Qwen/Qwen3.5-4B on stage1, then on stage2.
• Validation: validation rows were held out to monitor loss and were never trained on. The split is by object: an object's rows are either all in train or all in validation.
• Coverage: 10 categories, roughly balanced: animals; birds, sea creatures and bugs; containers and food; furniture; props; tools and gear; vehicles; architecture; fantasy and game; plants.

Columns

Column	Meaning
id	Row id, e.g. stage2-train-00000
category	One of the 10 categories
prompt	The user request
response	The exact assistant answer used as the training target

Each response has the same shape:

Asset: Accent cabinet
Triangle budget: 500
Brief: Narrow tall sideboard standing on four short tapered legs, front facing +Z: ...
Required named parts: carcass, top, door_left, door_right, knob_left, knob_right, leg_1, ...

```javascript
function buildAsset(THREE) { ... return group; }
```

How it was used for training

Every row was trained as one chat:

• System: system_prompt.md, included in this repo and identical for every row.
• User: prompt.
• Assistant: response.

The chat template was applied with enable_thinking=False, and loss was computed on the assistant answer only.

from datasets import load_dataset
from huggingface_hub import hf_hub_download

system = open(hf_hub_download("townbox/lowpoly-coder-4b-data", "system_prompt.md", repo_type="dataset")).read()
ds = load_dataset("townbox/lowpoly-coder-4b-data", "stage1", split="train")
ds = ds.map(lambda r: {"messages": [
    {"role": "system", "content": system},
    {"role": "user", "content": r["prompt"]},
    {"role": "assistant", "content": r["response"]},
]})

Notes

• Synthetic content: all text is model-generated. Requests and specs describe everyday objects, creatures, buildings and fantasy props in one fixed low-poly "woodland" style and palette.
• Code safety: the code was run in a sandbox to check it, but it is still untrusted. Run it isolated if you execute it.
• Licence: the stage1 answers are outputs of deepseek/deepseek-v4.1-flash-1017, and stage2 answers come from a model trained on them. Use is subject to that model's licence and terms, and to the terms of the services it was accessed through. No separate licence has been chosen for this compilation; see LICENSE.md.