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ProofOfConcept d484fd504c research: continual learning survey analysis — we're at the frontier 
Survey of 300+ papers confirms: nobody combines full-weight training +
Apollo + CUDA IPC + context-frozen + dream-loop curriculum + HOGWILD +
memory graph. Each technique exists; the combination is novel.

Key validations: flat-loss basin is our friend, 25% replay achieves
positive backward transfer, data quality > quantity, diversity >
regularization. Our multi-scale defense uses 3 of 5 CL technique
categories simultaneously — unprecedented in the literature.
2026-03-31 02:11:30 -04:00
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checkpoint checkpoint: sync live weights back into model safetensors in-place 2026-03-30 22:55:23 -04:00
research research: continual learning survey analysis — we're at the frontier 2026-03-31 02:11:30 -04:00
apollo_mini.py apollo: rewrite optimizer from paper's math + add research analysis 2026-03-31 00:54:17 -04:00
apollo_worker.py apollo: make rank configurable (default 1 = Mini, higher ranks for experimentation) 2026-03-30 22:06:31 -04:00
DESIGN.md DESIGN.md: complete rewrite reflecting validated architecture 2026-03-31 00:42:53 -04:00
export_weights.py apollo-mini training system: initial implementation 2026-03-30 22:02:37 -04:00
first_training_step.py first_training_step.py: ready for Kent to run 2026-03-31 01:59:52 -04:00
start_vllm_with_apollo.sh vllm launcher with apollo hook 2026-03-30 22:24:02 -04:00
train.py apollo-mini training system: initial implementation 2026-03-30 22:02:37 -04:00
training_example.py apollo-mini training system: initial implementation 2026-03-30 22:02:37 -04:00
vllm_export_hook.py apollo-checkpoint: efficient diff-based GPU weight checkpointing 2026-03-30 22:53:17 -04:00
weight_mapping.py weight_mapping: strip language_model prefix to match HF text model names 2026-03-30 23:11:03 -04:00