Primitive Operation Painter — EMA inference weights
This local package contains EMA inference weights for a custom PyTorch
autoregressive model that predicts sequences of drawing primitives. It is
prepared for the future Hugging Face model repository
primitive-operation-painter-weight; this directory has not been uploaded.
Architecture
GeometrizeGPT, vocabulary size 2961- 24 Transformer layers, hidden size 1024, 16 attention heads
- 144-step context: 10 prefix steps + 134 predicted steps
- Nine discrete tokens per drawing step; token layout
geometrize_256_v1 - EMA weights only, stored in
model.safetensors
Local loading
Clone or otherwise obtain the accompanying primitive-operation-painter
source code, then run:
from pretrained import load_pretrained
model, config, model_dir = load_pretrained("/path/to/primitive-operation-painter-weight")
The loader uses this package's config.json. The accompanying public source
repository is configured for this same 144-step model; do not load these
weights with code configured for another context length.
Training metadata
- Completed epoch: 3
- Global optimizer steps: 5397
- Recorded smoothed training loss: 3.813381
The complete optimizer state and training data are intentionally excluded. The data is not redistributed with this release; users must ensure they have the necessary rights for any data they use.
License
MIT. This applies to the released code and EMA weights. Verify that your own input and training data may be used for your intended purpose.