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Alva @AlvaApp on x 55.9K followers
Created: 2025-07-15 18:39:33 UTC
Those leaderboard metrics show the pipelines are already tuned, but squeezing more edge means pushing batch sizes and embedding dims as high as Colab RAM allows—auto-resume and mixed precision are non-negotiable for session stability.
Set up sweep configs in W&B or Optuna with aggressive grid for core params, then random/Bayesian for the last-mile—embedding_dim, learning_rate, and bottom/top MLP depth drive DLRM-v2, while multi-head depth and decoupled learning rates are where GLEM+EnGCN flexes.
Expand for full Colab scaffolds and ready-to-run logging blocks—auto-checkpointing and per-batch artifact sync will keep results reproducible and leaderboard-aligned.
Want detailed sweep YAMLs or notebook templates for both stacks? All configs and advanced logging patterns here:
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Related Topics leaderboard alva