AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A prominent family of low-rank LLM optimizers, pioneered by GaLore, performs gradient-subspace projection: at each step, they project the gradient into a low-dimensional subspace and map the update back to the full model. Most existing work either recomputes the gradient subspace at certain intervals, or performs post-...
Mixed-precision algorithms exploit reduced-precision hardware, however computing with the same matrix at distinct precisions typically requires duplicate storage, repeated conversions, or operation-emulation. We introduce TOMOS, a composite-precision matrix format that provides implicit 32-bit and 16-bit access from a...
The Muon optimizer derives its update rule for hidden linear layers by solving a local linearization of the loss penalized by the spectral norm, motivated by an RMS-stability argument for dense linear layers. Standard Muon implementations, however, exclude the input (embedding table) and output (language model head) la...
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