Individual skill
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.
skills/vendor-ai-research/ml-training-recipes/SKILL.md
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- agent-skills Repository topic
- agents Repository topic
- ai Repository topic
- ai-agents Repository topic
- ai-scientist Repository topic
- artificial-intelligence Repository topic
- autonomous-agents Repository topic
- autonomous-research Repository topic
- autoresearch Repository topic
- claude-code Repository topic
- pytorch Declared skill tag
- training Declared skill tag
- optimization Declared skill tag
- llm Declared skill tag
- vision Declared skill tag
- diffusion Declared skill tag
- biomedical Declared skill tag
- muon Declared skill tag
- adamw Declared skill tag
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- testing Derived category
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SKILL.mdcore · 11,483 bytesSKILL.mdcore · 11,483 bytesSKILL.mdcore · 11,483 bytesSKILL.mdcore · 11,483 bytesSKILL.mdcore · 11,483 bytesSKILL.mdcore · 11,483 bytesSKILL.mdcore · 11,483 bytesreferences/architecture.mddoc · 10,523 bytesreferences/biomedical.mddoc · 21,275 bytesreferences/domain-specific.mddoc · 19,643 bytesreferences/experiment-loop.mddoc · 4,574 bytesreferences/optimizers.mddoc · 10,893 bytesreferences/scaling-and-selection.mddoc · 17,259 bytes
Read excerpt
# ML Training Recipes
Battle-tested patterns for PyTorch training across domains. Drawn from production codebases
(Karpathy's autoresearch/nanochat, torchvision, HuggingFace) and modern training practice.
## Reference files (read when needed)
- `references/architecture.md` — Transformer/LLM architecture code patterns, weight init
- `references/optimizers.md` — Muon, AdamW hybrid, per-group LR, compiled optimizer steps
- `references/domain-specific.md` — Vision, diffusion, contrastive, distributed, checkpointing, data loading
- `references/scaling-and-selection.md` — Scaling laws, compute budget tables, decision trees, DGX Spark
- `references/biomedical.md` — Drug discovery, protein models, medical imaging, genomics, clinical NLP
- `references/experiment-loop.md` — Autonomous experiment loop (autoresearch keep/discard/revert)
