chemistry-ablations-and-curves-without-an-accelerator
SolidUse at study design after you have priced a scaled-down training arm and found the machine cannot carry it — no accelerator visible, or no wall clock for one arm. Covers the one-row-per-named-component table with the inference switch that removes each part, why an input ablation does not answer a component criterion, and the ladder of curves that still ships when nothing can be trained.
Install
Quality Score: 85/100
Skill Content
Details
- Author
- tangxiangru
- Repository
- tangxiangru/AutoR
- Created
- 5 months ago
- Last Updated
- today
- Language
- Python
- License
- NOASSERTION
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
train-the-named-architecture
Use at study design, implementation and experimentation when the brief's deliverable is a model you have to build — it names an architecture family (graph network, autoencoder, diffusion module, surrogate net) or a training regime (pre-training, fine-tuning, self-supervised, inverse design). Covers why a cheaper model class scores near zero however well it performs, why a scaled-down run of the named architecture beats a released checkpoint on every architecture criterion, and what to ablate.
ablate
Use for /ablate and $ablate. Finds which parts of a SKILL.md earn their tokens and rebuilds the skill without the rest. Use when asked to ablate, audit, shrink, slim, or trim a skill, when skill bloat or per-session context cost comes up, or to work out which sections of a skill are load-bearing.
ablation-design
Design ablation studies to isolate component contributions in ML systems