Clifford SSM research checkpoints
This repository archives experimental Clifford SSM / Wave Unified byte-language-model checkpoints produced by the Harmonic GPT research project.
These are research artifacts for studying self-organization in neural computation. They are not presented as production-ready language models. The architecture uses oscillator/manifold dynamics rather than a standard Transformer stack.
Contents
The original experiment-directory structure is preserved:
e23_nano_omni_plateau_resumee24_chinchilla_5k_cool_repair_7500e24_chinchilla_5k_cool_repair_lowlr_warm2ke24_chinchilla_5k_cool_repair_lrsweep_qtre24_chinchilla_5k_cool_repair_minlr5e6_9000e24_chinchilla_5k_cool_repair_minlre6_9000e24_chinchilla_latest_governed_5ke24_e23best_chinchilla_coldadapt_v2_containere25_quality_80m_chinchillae25_quality_fresh_smokee26_v2classified_125m_continue
Most files are PyTorch training checkpoints. The e23 directory also includes a NumPy export and a detailed tensor manifest. SHA256SUMS records every uploaded artifact checksum.
Loading and safety
Use the matching Harmonic GPT / Clifford SSM source revision and architecture configuration. These checkpoints are not directly compatible with transformers.AutoModel.
PyTorch .pt files use pickle-based serialization. Only load them in a trusted environment and review the loader path first. Prefer the .npz export when it satisfies your use case.
Data and limitations
The checkpoints were produced across multiple experimental corpus and continuation regimes, reflected in the directory names. Training datasets are not included. Results from different directories should not be treated as matched comparisons unless the corresponding experiment documentation establishes matching.
No claim is made that these models are suitable for deployment, safety-critical use, or generating factual information.
License
Released under the MIT License used by Harmonic GPT. See the upstream repository for source and architecture documentation.