| Models |
PRISME-tiny (2.1M / 5.6M char-level synthetic backbones + 0.17M adapters), PRISME-103M (99.5M backbone + 2.63M workspace/controller). 459M and 1.18B: configuration files and meta-device instantiation only (not trained). |
| Architecture |
Pre-norm causal decoder (RMSNorm, RoPE, GQA, SwiGLU, tied embeddings) + read-only source bank + 16-slot ephemeral workspace + shared 2-block sparse editor (24 actions + STOP, cap 8) + value controller + differential readout (docs/architecture_spec.md). |
| Training data |
tiny: synthetic suite v1 train split (23,005 items); 103M: FineWeb-Edu sample-10BT subset, 196.6M tokens (data/processed/fineweb_edu_300m/manifest.json). |
| Intended use |
Research artefact for reproducing the WP31 experiments. Not a general-purpose language model; the 103M backbone saw two tokens per parameter. |
| Out of scope |
Any deployment; generation of text for end users; evaluation on benchmarks it was not pre-registered for. |
| Known limitations |
Frozen-backbone adapters; learned routing is worse than no routing on held-out synthetic data (negative result); bank memory grows linearly with context. |
| Checkpoints |
experiments/runs/<run_id>/checkpoint/{model.safetensors, checkpoint.json} on the node that trained them (run index in reproducibility/run_index.md); laptop copies for the tiny models. Weights SHA-256 in each checkpoint.json. |
| Licence |
Code MIT; weights CC BY 4.0; corpus-derived artefacts inherit ODC-By 1.0 attribution. |