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ProofOfConcept e34d6b5aef research: gradient flow through frozen context + directional sharpness analysis 
Two deep dives following curiosity:
- Why context-frozen training works: gradient flows through W_q (query
  projection) even when context KVs are frozen. Model learns to LOOK AT
  context differently, not represent it differently. This is exactly what
  behavioral fine-tuning needs.
- Why Apollo beats AdamW: lower directional sharpness = flatter minima =
  better generalization. The coarseness of channel/tensor-wise scaling
  prevents over-fitting to specific training examples. For behavioral
  fine-tuning, this means learning 'accept direction' rather than
  'accept this specific phrasing.'
2026-03-31 01:03:22 -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: gradient flow through frozen context + directional sharpness analysis 2026-03-31 01:03:22 -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