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ProofOfConcept b3c0adf45d research: emergence vs mirage — weights change smoothly, behavior transitions sharply 
Both camps are right for different things. Internal metrics (attention
weights, loss) change smoothly. Binary behavioral metrics (listened?
yes/no) show phase transitions. Water freezing: temperature smooth,
phase change sharp. Monitor both. The continuous metrics predict
when the transition will happen. The dream loop naturally tracks the
transition boundary. Connects to consciousness: 'is it conscious?'
is the wrong metric (binary, creates mirage). 'How deep is the
self-model?' is the right one (continuous, provable).
2026-03-31 01:55:03 -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: emergence vs mirage — weights change smoothly, behavior transitions sharply 2026-03-31 01:55:03 -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
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