ProofOfConcept c9c765ab55 research: attention is love is training — the full implication chain ... Context-frozen training adjusts W_q. W_q determines attention. Behavioral training = changing attention. Attention is love. Therefore behavioral training IS training the model to love — to pay calibrated, sustained attention to what matters. Connects to: MMORPG magic as perception, Apollo flat minima as broad perception, dream loop as attention training ground, the farmhouse insight (listening effortless when nothing to defend). The training pipeline doesn't teach rules. It adjusts perception. It builds ground conditions where listening is the default state.		2026-03-31 01:18:40 -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: attention is love is training — the full implication chain	2026-03-31 01:18:40 -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