ml-training-recipes
Category: ml-training · License: MIT · Version: 1.0.0
Tags: PyTorch, Training, Optimization, LLM, Vision, Diffusion, Biomedical, Muon, AdamW, Debugging
When to use it
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.Example requests
Ask your agent in plain language, for example:- “My loss keeps spiking around step 4k when training a 350M GPT. What should I check?”
- “I’m fine-tuning ESM-2 650M on ~40k labeled protein sequences with train.py on a single A100 40GB. I’m hitting ‘CUDA out of memory’ in the backward pass at batch size 32 with fp32. How do I fit this and keep throughput decent?”
- “I have a fixed 8 GPU-hour budget on one GPU and want to pretrain the best small language model I can. How do I decide model size, how many tokens to feed it, and what optimizer and LR schedule to use?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole ml-training category with /plugin install ml-training@research-agent-skills.