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.
npx skills add OpenRaiser/NanoResearch --skill ml-training-recipes --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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) --- ## Architecture Selection Pick the right model by **data type** and **data scale**: | Data Type | < 10K samples | 10K-100K | > 100K | |-----------|--------------|----------|--------| | **Images** | Pretrained CNN + fine-tune | Fine-tune ViT or CNN | ViT from scratch | | **Text (gen)** | Few-shot prompting | Fine-tune GPT/LLaMA (LoRA) | Pretrain from scratch
- Reference files (read when needed)
- Architecture Selection
- Scaling Laws
- Chinchilla rule (Hoffmann et al., 2022)
- Training Loop
- Key principles
- Optimizer Configuration
- Rules of thumb
- Learning Rate Scheduling
- Time-based (autoresearch style)
- Cosine decay
- Guidance
- Mixed Precision & Compilation
- Memory & Performance
What does the ml-training-recipes skill do?
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.
How do I install it?
Run `npx skills add OpenRaiser/NanoResearch --skill ml-training-recipes --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From OpenRaiser/NanoResearch, a repository with 1,480 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.