mirror of
https://github.com/wassname/ml-debug.git
synced 2026-09-26 14:00:26 +08:00
wassname
c9c53f8e7f
nanochat_deepwiki_llm_pretraining_2026.md rewritten with content from dev/LOG.md and deepwiki sections 3/12/13: - 14 labelled findings with direct quotes and empirical numbers - Dataset >> architecture (27% gain, 5 failed attempts before ClimbMix) - Scale-dependent HP sensitivity (d12 HPs hurt d20) - Multi-axis validation (steps/wall-clock/FLOPs) - Negative results: MoE/SwiGLU/MTP all failed at this scale - MFU monitoring, batch size Bopt∝D^0.383, WD∝1/width² tables - FP8 reality: 1.38x micro → 1.17x full → 5% capability-matched - Python GC 500ms overhead, torch.compile recompile gotcha karpathy_nn_zero_to_hero_lec4_diagnostics.md: new evidence file - Activation saturation check (tanh >0.97) - Gradient distribution check per-layer - Grad:data ratio (target ~1e-3) - Update-to-data ratio tracker with full plotting code - Incremental improvement log from notebook
ML Debugging Folklore
Practitioner knowledge for debugging ML systems, curated and synthesized by wassname. Opinionated by source selection -- I picked sources I trust (Schulman, Goodfellow, CS231n, ...) and had an LLM extract the most relevant information for debugging ML systems.
Use as a Claude skill
/skills add https://github.com/wassname/ml_debug
Or paste SKILL.md into your system prompt / context when debugging.
What's here
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SKILL.md -- the main artifact. Load into an LLM agent's context as a debugging skill. Parts 1-5 are reference knowledge; Part 6 is a runnable triage protocol (grep patterns, diagnostic snippets, decision tree); Part 7 is debugging mental models and practitioner priors.
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docs/evidence/ -- frozen local copies of source material (blog posts, talks, papers, reddit threads). Claims in SKILL.md link back to exact quotes here.