spb/zyquo-mlx Public MIT
The local MLX foundry for your Mac — run, fine-tune, quantize, and ship models. Nothing leaves your machine.
Swift 93.4%
Python 3.8%
Makefile 2.2%
Shell 0.5%
1//2// HyperParams.swift3// Zyquo MLX4//5// Author: Simon-Pierre Boucher6// Mail: contact@spboucher.ai7//89import Foundation1011/// Training hyperparameters. Field names/defaults mirror mlx-lm's12/// `CONFIG_DEFAULTS` exactly (docs/TRAINING-RESEARCH.md §1.2); serialized to13/// the YAML config the Python bridge consumes (`lora_parameters` and14/// `lr_schedule` are config-only upstream).15struct HyperParams: Codable, Hashable, Sendable {16 // LoRA-specific (config-only upstream)17 var rank: Int = 818 /// MLX uses a single scale factor instead of alpha/rank (default 20.0).19 var scale: Double = 20.020 var dropout: Double = 0.021 /// Target modules; nil = adapt all linear layers in targeted blocks.22 var keys: [String]?2324 // Core loop25 var numLayers: Int = 16 // -1 = all layers26 var batchSize: Int = 427 var iterations: Int = 100028 var learningRate: Double = 1e-529 var maxSeqLength: Int = 204830 var seed: Int = 03132 // Optimizer: adam | adamw | muon | sgd | adafactor33 var optimizer: String = "adam"34 var gradCheckpoint: Bool = false35 var gradAccumulationSteps: Int = 136 var maskPrompt: Bool = false3738 // Cadences39 var stepsPerReport: Int = 1040 var stepsPerEval: Int = 20041 var saveEvery: Int = 10042 var valBatches: Int = 2543}44