delta_net_100M_r8_bs256_lr3e-4_steps5000

DeltaNet 100M (rank 8) — Low-rank Fast-Weight Ablation

Pretrained 100M-parameter DeltaNet with low-rank parameterization (r8) on FineWeb-Edu. Part of a 16-cell ablation (4 archs × 4 ranks: r8, r32, r64, rfull) studying whether constraining the q/k/v fast-weight projections (or LaCT's SwiGLU MLP) to low rank can match or exceed full-rank performance.

Training

Code: see run_main_100M.sh.

Eval results

• FineWeb-Edu val PPL: 28.54
• MQAR (multi-query associative recall):
  • K=4: 0.360
  • K=16: 0.410
  • K=64: 0.798
  • K=256: 0.948
• LAMBADA acc: 0.033
• HellaSwag acc_norm: 0.276
• ARC-Easy acc_norm: 0.392
• PIQA acc_norm: 0.584
• WinoGrande acc: 0.489

Notes

• This is one of 16 cells; the other rank/arch combinations are uploaded under the same HF org (nlproj) with repo names matching the local dump folder, e.g. nlproj/delta_net_100M_{r8|r32|r64|rfull}_bs256_lr3e-4_steps5000.
• Key finding of the ablation: at this scale, low rank often matches or beats full rank on downstream tasks (LoRA-style "adaptation is intrinsically low-rank" hypothesis). GatedDeltaNet is the exception — its rfull is the strongest in the whole sweep on PPL / LAMBADA / HellaSwag / ARC-Easy.

Run name: delta_net_100M_r8_bs256_lr3e-4_steps5000