Welcome back, Tharun
Strange Loop is AI and can make mistakes. Please double-check responses and results.
A conv stem trades a few patch-embed params for an inductive bias that usually helps small ViTs on small images. The cleanest test holds everything else fixed and varies the stem width against the learning rate. I'll hand that to a GPU subagent so it can own the training loop end to end, and run the seeded baseline alongside it.
A conv stem is a well-supported tweak for small ViTs at this resolution, but the reported gains are within seed noise at CIFAR scale — so it needs a baseline to beat, not just a sweep.
I've drafted two experiments. Review either plan, leave comments inline, and approve to launch. They'll run concurrently on their own GPUs.
Best configuration: conv stem width 64 at lr 8e-4 — validation top-1 0.747 vs the seeded patchify baseline of 0.726 ± 0.004 (+2.9% relative). The gain concentrates in the first 30 epochs and holds on all three baseline seeds. Wall clock 41m on one H100.
Both runs are in. The conv stem is a real win at this scale: 0.747 top-1 against a 0.726 ± 0.004 baseline, comfortably outside seed noise, at no extra training cost. Width 64 is the peak — 96 regresses.
Projects
Push a small ViT past the patchify baseline on CIFAR-100 — architecture + training-recipe search.
tharunkumartk/vit-labOptimizer, warmup, and schedule ablations for a small GPT-2 — what actually moves val/loss at fixed compute.
tharunkumartk/nanogpt-labSettings
Link tokens and environment variables. They're injected into your chat agent and your subagents' pods at launch — so gated model downloads and authenticated tools just work.
Choose how Strange Loop looks. System follows your device setting.
Which cluster your subagents launch on. Takes effect for subagents you start from now on; running ones stay on their current platform.
Stored as HF_TOKEN for gated/private model and dataset access.
Any other secrets your runs need — e.g. WANDB_API_KEY, OPENAI_API_KEY.
- WANDB_API_KEY••••3b1c
- HF_HUB_ENABLE_HF_TRANSFER••••1
Good — but also log the best-epoch number so we can quantify how much early stopping would have flattered the result.