research-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...
Source facts
- Repository
- GrayCodeAI/starling
- Last source activity
- July 13, 2026 at 09:28
- Detected SKILL.md language
- English
- Stars
- 2
- Forks
- 0
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Source instructions · Read-only preview- name
- research-ml-training-recipes
- description
- 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...
- license
- MIT
- tags
- ["pytorch","training","optimization","llm","vision"]
- version
- 1.0.0
- author
- dailycafi
- dependencies
- ["torch>=2.0.0"]
## Experiment Management
Track experiments in TSV for easy comparison:
```
commit val_bpb memory_gb status description
a1b2c3d 0.9979 44.0 keep baseline
b2c3d4e 0.9932 44.2 keep increase matrix LR to 0.04
c3d4e5f 1.0050 44.0 discard switch to GeLU (worse)
```
**Simplicity criterion**: all else equal, simpler is better. Removing something and getting equal
results is a great outcome. For systematic agent-driven experimentation, see `references/experiment-loop.md`.
### Evaluation metrics by domain
| Domain | Primary Metric | Notes |
|--------|---------------|-------|
| LLM | BPB (bits per byte) | Vocab-size-independent |
| Classification | Accuracy / F1 | Macro-F1 for imbalanced |
| Segmentation | mIoU / Dice | Per-class IoU reveals weak spots |
| Generation | FID | Needs >10k samples |
| Regression | RMSE / MAE | Log-transform skewed targets |
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