| --- |
| license: mit |
| tags: |
| - text-generation |
| - css |
| - synthetic-data |
| - custom-model |
| --- |
| |
| # DivCenterer |
|
|
| A deliberately tiny, single-task decoder-only Transformer that generates CSS to center one div with either CSS Grid or Flexbox. |
|
|
| ## Model |
|
|
| - 1,107,216 parameters; 4 decoder layers, width 144, 4 attention heads, SwiGLU width 384. |
| - Trained from scratch with BF16 autocast and PyTorch SDPA on an AMD Radeon RX 9070 XT. |
| - Custom 768-token constrained byte-BPE tokenizer; this is not a Transformers `AutoModel` checkpoint. |
| - Files: `model.safetensors` (weights), `config.json`, `tokenizer.json`, `model.py`, and `tokenizer.py`. |
|
|
| Load the weights with the included implementation: |
|
|
| ```python |
| import json |
| import torch |
| from safetensors.torch import load_file |
| from model import DivCentererLM, ModelConfig |
| |
| with open("config.json", encoding="utf-8") as stream: |
| config = ModelConfig(**json.load(stream)) |
| model = DivCentererLM(config) |
| model.load_state_dict(load_file("model.safetensors")) |
| model.eval() |
| ``` |
|
|
| ## Data and evaluation |
|
|
| The model was trained on a deterministic synthetic dataset. The prompts and HTML are generated from project-specific templates and word lists; no downloaded corpus or pretrained model weights were used. The model and dataset were developed with AI assistance. |
|
|
| Greedy exact-CSS results on the full synthetic evaluation suite: |
|
|
| | Split | Exact | |
| | --- | ---: | |
| | Validation | 410/410 | |
| | Held-out pairs | 410/410 | |
| | Unseen values | 64/64 | |
| | Held-out phrasing | 96/96 | |
| | **Total** | **980/980** | |
|
|
| These scores measure exact matches against the generated dataset's canonical CSS answers. |
|
|
| ## License |
|
|
| The files in this repository are licensed under the MIT License. See [LICENSE](LICENSE). |
|
|
