ml-training-recipes
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
- Version
- 1.0.0
- License
- MIT
Pinned to revision df088027ff23, so it is the text this page describes rather than whatever the author pushed since.
Files
- skills/ml-training-recipes/SKILL.md
- skills/ml-training-recipes/references/architecture.md
- skills/ml-training-recipes/references/biomedical.md
- skills/ml-training-recipes/references/domain-specific.md
- skills/ml-training-recipes/references/experiment-loop.md
- skills/ml-training-recipes/references/optimizers.md
- skills/ml-training-recipes/references/scaling-and-selection.md
Every link opens the file at its source, pinned to the revision this page describes.