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Online Hebbian v10 Growing 32->528 mE16

This repo contains the Lightning checkpoint for the mlp-mixer Online Hebbian v10 growing-memory language-model run used in the experiment notes as 32->128, 32->256, and 32->528.

Those labels are eval-time linear active-feature schedules applied to the same trained checkpoint, not three separately trained model files. The trained schedule starts with 32 active bilinear features and expands by 16 features every 64 tokens, reaching 528 features at L=2048.

Provenance

  • W&B: https://wandb.ai/hazy-research/mlp-mixer/runs/m05d01y26-oh-v10-growing-mE16-full
  • W&B path: hazy-research/mlp-mixer/m05d01y26-oh-v10-growing-mE16-full
  • Training run name: m05d01y26-online-hebbian-v10-growing-noSiLU-initSmall-120m-lr=0.0005-layers=12-dmodel=768-heads=12-initstd=0.02-miF=32-mEToks=64-memexpansion=16
  • Source checkpoint on Perlmutter: /pscratch/sd/j/jwl50/online-mlps/artifacts/checkpoints/mlp-mixer-pile/m05d01y26-online-hebbian-v10-growing-noSiLU-initSmall-120m-lr=0.0005-layers=12-dmodel=768-heads=12-initstd=0.02-miF=32-mEToks=64-memexpansion=16/last.ckpt
  • Hydra experiment: mlp_mixer/online_hebbian_v10_noSiLU_initSmall_growing_120m

Files

  • lightning/last.ckpt: original PyTorch Lightning checkpoint.
  • training_config.yaml: exact saved Hydra config from the training run.
  • training_config_composed*.yaml: composed export configs retained for reproducibility.
  • wandb_summary.json, wandb_config.json, wandb_metadata.json: W&B metadata captured at export time.

Latest Logged Metrics

Metric Value
trainer/global_step 16299
train/num-tokens_step 8545894400
train/ppl_step 12.028423824457565
val/ppl 10.840018093394374
val/loss 2.3832449913024902
val/num-tokens 6133383168

Loading

Use the HazyResearch/mlp-mixer codebase with the Online Hebbian v10 growing mixer available. The checkpoint is the Lightning checkpoint, so instantiate the model from training_config.yaml / the Hydra experiment and load lightning/last.ckpt with the repo checkpoint-loading utilities.

The portable eval scripts can evaluate the same file under different active-feature schedules; the experiment-note labels correspond to 32->128, 32->256, and 32->528 linear schedules.

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