Apple Silicon dual-backend port of autoresearch (PyTorch MPS + MLX) with full Muon optimizer
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Updated
Oct 19, 2026 - Python
Apple Silicon dual-backend port of autoresearch (PyTorch MPS + MLX) with full Muon optimizer
Unofficial Optimized Implementation for NorMuon
From-scratch 125M LLM: 300K steps, 57.8B-token corpus, 282K tok/s on 2x H100
LLM pretraining from scratch on FineWeb dataset (architecture and all components explained), plus optimal use of GPU on SLURM cluster
PyTorch DDP and multi-GPU LLM training — from a minimal distributed example to NanoGPT speedruns, Muon, H100 profiling, and modded-nanogpt. FBA LAB https://bubblnet.com
Megatron-LM fork for experiments on Alps
A performance-optimized Muon optimizer implementation for PyTorch
High-performance CUDA implementation of Muon optimizer for LLM training. Features Newton-Schulz polar decomposition, cuBLAS acceleration, and transpose optimization for 8x FLOP savings on transformer FFN layers. Benchmarked on NVIDIA A100 with Llama 3.1 8B architectures (4096×11008 weights).
Don't tune the knee. Measure it.
🤖 Ultron (113M) — A SOTA LLM pre-trained from scratch on 10B FineWeb-Edu tokens featuring the Muon Optimizer, RoPE, GQA, SwiGLU, and zero-copy streaming. "There are no strings on me."
Hybrid Muon + AdamW optimizer with custom fused Triton kernels for fast, drop-in training.
OpenAI parameter-golf challenge: train the smallest LM that fits in 16MB. My SP8192 frontier submission (openai/parameter-golf#1887): 11L x 512d w/ targeted middle recurrence, MuonEq-R optimizer, int6 GPTQ+SDClip, Brotli-11 compression, legal score-first TTT. 8xH100 DDP.
≈125M decoder-only transformer from first principles with RoPE, RMSNorm, QK-Norm, and Muon + AdamW.
Few-Shot Adaptation for Vision-Language Models. Implements Base-to-Novel generalization on CLIP using LoRA, LP++, and Muon Optimizer to enhance performance on the Oxford Flowers-102 dataset.
A controlled, single-GPU benchmark of modern GPT architecture against the original GPT2.
ARS: Slide directly with the geodesic of the loss landscape to the global optimum.
Exploring new trendencies in numerical optimization applied on PINNs. An example with the nonlinear TDSE.
NerVE: Nonlinear Eigenspectrum Dynamics in LLM Feed-Forward Networks (ICLR 2026)
nanoWM: a pose-conditioned causal Diffusion Transformer world model at nano scale, built to measure which training tricks (REPA, Muon, muP, diffusion forcing) pay off in 20 ticketed GPU-hours on Kaggle T4s. The deliverable is the measurement, not the model.
Official code for optimizer-induced spectral scaling laws
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