spb/forge-studio Public
The Instruments of LLM training — a native macOS cockpit for Forge. Train language models from scratch on Apple Silicon without a terminal.
Swift 95.7%
Shell 4.3%
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Forge Studio M1: SwiftPM app skeleton, full config codec, run supervisor, live dashboard
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- RESEARCH.md: ground-truth Forge contract (config schema incl. waves 1-3, CLI, log.csv grammar with elapsed_s, checkpoint/resume, signals) - ForgeConfig: byte-compatible Codable mirror of every model/train field, param-count formula cross-checked against forge info, LR-schedule math - Services: ProcessRunner (actor, line streaming), LogParser (header-driven CSV + stdout events), MetricsStore (actor, incremental CSV tail, LTTB snapshots), RunStore (atomic registry), RunSupervisor (state machine, launch/stop, 1 Hz ingest), ForgeBinaryLocator, SystemInfo - Charts: LTTB downsampler, bias-corrected EMA - UI: NavigationSplitView shell, run rows/badges, RunDetail dashboard (loss raw+EMA+val, log-Y, best-val marker, LR/tok-s/grad-norm secondary charts, console), New Run sheet (presets, derived panel, LR preview, inline validation), Settings with forge-info validation - scripts: package-app.sh (SPM -> .app, ad-hoc) and notarize.sh with the real zyquo-term identity (Team 3YM54G49SN, profile MacLustr-Notarize) - 8 tests green: config round-trip vs real configs, param count, LTTB, EMA, CSV/stdout parsers, state machine, LR preview Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>