vukrosic / muon-optimizer-guide

What is muon-optimizer-guide?

This GitHub muon optimizer guide helps Python developers swap AdamW for the Muon optimizer in LLM pretraining, delivering faster convergence on PyTorch models. It bundles ready-to-run training scripts for small LLMs (up to 88M params), dataset download snippets for 40M-2B tokens, and benchmarks on ARC-Challenge, HellaSwag, and GSM8K. Users get wall-clock speedups shown in loss curves, plus a course explaining the muon optimizer algorithm.

Why is it gaining traction?

Muon beats AdamW on training speed—reaching target loss in half the steps—while matching perplexity, as seen in PyTorch muon github experiments versus adamw baselines. Big labs like DeepSeek and OpenAI use it, and PyTorch 2.10 added official support, making this a practical drop-in for muon optimizer torch or JAX setups. The hybrid Muon+AdamW approach optimizes memory and handles diffusion or clip models without extra tuning.

Who should use this?

LLM researchers pretraining from scratch on consumer GPUs, especially those benchmarking muon optimizer vs adamw on tasks like commonsense reasoning. PyTorch devs tuning optimizers for Keras/TensorFlow ports via muon optimizer optax, or teams exploring super muon github variants for tomography-style detection pipelines. Ideal for quick experiments scaling to 1B tokens without infra headaches.

Verdict

Solid guide for trying Muon over AdamW, but at 44 stars and 1.0% credibility score, it's early-stage—docs are guide-focused but lack extensive tests. Worth forking for PyTorch LLM baselines if you're optimizer-curious.

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