# 🔥 Forge Studio ### The Instruments of LLM training — a native macOS cockpit for [Forge](https://github.com/spboucher-ai/forge) **Train language models from scratch on Apple Silicon without ever opening a terminal.** Pick a dataset, shape an architecture, hit Start — and watch the loss fall on a silky live dashboard. Swift + SwiftUI + Swift Charts · macOS 14+ · Apple Silicon · zero third-party dependencies

--- ## ✨ What it is [Forge](https://github.com/spboucher-ai/forge) is a from-scratch C++20 + Metal LLM training framework: its own tensors, autograd, flash attention, Muon optimizer, MoE, QAT, and the `.forge` zero-copy weight format. **Forge Studio is its native GUI companion** — the whole train-a-model workflow, end to end, as a first-party-feeling Mac app: 1. **Prepare data** — TinyStories or streamed Hugging Face mixtures (FineWeb-Edu, DCLM, Cosmopedia, research presets), with a live console 2. **Design a model** — every Forge config field, from `n_layers` to DeepSeek-style MoE routing, with live validation 3. **Train** — launch, monitor, stop; runs survive app crashes without lying 4. **Understand** — a loss dashboard built to the TensorBoard/W&B standard, native 5. **Compare** — multi-run overlays on the *tokens* axis, the honest one 6. **Use the model** — generate and evaluate from any checkpoint, in-app Forge Studio never reimplements training. It drives the real `forge` binary and reads its structured metrics — what you see is exactly what the framework did. ## 📊 The dashboard The centerpiece. Built on Swift Charts, fed by an actor-isolated metrics store: - **Raw + smoothed loss** — low-opacity raw train line under a bias-corrected EMA (TensorBoard semantics, 0→0.99 slider), val loss as connected points - **Hover crosshair** with a full callout: step, train, EMA, perplexity, val - **Pinch-zoom & pan** on X, double-click to reset, and a **"follow live ⏵" pill** that re-pins the window to incoming data after you've panned away mid-run - **Best-val marker** — dashed rule + `best val 2.146 @ step 999` annotation - **Log/linear Y**, secondary charts for LR schedule (watch warmup + cosine/WSD actually happen), tokens/sec, grad norm with the `grad_clip` threshold drawn - **Scale discipline**: raw data is never discarded; the UI reads LTTB-downsampled snapshots (~2× pixel width). Measured: 200,000 CSV rows ingest in ~1.1 s and snapshot to chart width in <250 ms — hover stays hitch-free on 100k-step runs ## 🛡️ Runs that can't lie - Single-writer **state machine** (`queued → launching → running → … → finished | failed | stopped`) with an explicit legal-transition table — illegal transitions are unrepresentable - **Atomic persistence** (temp-file-then-rename) for the registry: `kill -9` the app whenever you like - **Crash recovery**: runs persisted as active at launch are truthfully resolved — including the case where the forge process is *still alive* — with an honest explanation and the recovery path (`ckpt_latest.bin`) - **Watchdog**: a "possibly stalled" badge when metrics stop flowing for 30 s - Stop is SIGTERM and the UI tells you exactly what that means (Forge doesn't checkpoint on signals; you lose at most `checkpoint_every` steps) - Local **notifications** with the final loss when a run finishes or fails ## 🧪 Ground truth, tested `RESEARCH.md` documents the full Forge contract (config schema, CLI, `log.csv` grammar, checkpoint/resume, signal behavior) extracted from the source. The test suite enforces it: - `ForgeConfig` round-trips the **real** `configs/*.json` from the forge repo, byte-compatible field names - The Swift param-count formula matches `forge info` for every shipped config - CSV/stdout parsers are exercised against real and mutated lines (legacy 6-column headers, garbage, truncation) - LTTB invariants (endpoints exact, monotonic X), EMA bias correction, state-machine table, LR-preview math vs `scheduler.h`, 200k-point stress test ## 🚀 Quick start ```bash # 1. Build Forge (once) git clone https://github.com/spboucher-ai/forge && cd forge cmake -B build && cmake --build build -j # 2. Run Forge Studio # — from the DMG on the Releases page, or from source: git clone https://github.com/spboucher-ai/forge-studio && cd forge-studio ./scripts/package-app.sh release && open dist/ForgeStudio.app ``` In **Settings**: point to `forge/build/forge` (validated live via `forge info`) and pick a workspace. Then **Datasets → New dataset** (TinyStories at vocab 4096 takes a few minutes), **＋ New run**, pick the `gpt-10m` preset, Start — first loss point lands within seconds. ## 🏗️ Architecture | Layer | What lives there | |:--|:--| | `Models/` | `ForgeConfig` (Codable mirror of every Forge field + validation + derived math), `Run` (state machine), `MetricPoint`, `Dataset` (bin-header readers) | | `Services/` | `ProcessRunner` (actor, incremental line streaming), `LogParser` (header-driven CSV + stdout events), `MetricsStore` (actor, incremental tail, LTTB snapshots), `RunStore` (atomic registry), `RunSupervisor`, `ForgeBinaryLocator`, `SystemInfo` | | `Charts/` | `TrainingChartView` (the interactive dashboard), `Downsampler` (LTTB), `Smoothing` (bias-corrected EMA) | | `Views/` | Navigation shell, run dashboard, New Run editor (presets, derived panel, LR preview), Compare, Datasets, Checkpoints/Generate/Eval, Settings | | `scripts/` | `package-app.sh` (SwiftPM → .app), `generate-icon.sh`, `notarize.sh` (Developer ID + hardened runtime + DMG + notarytool + stapler) | Swift Concurrency throughout: the process I/O and metrics paths never touch the main thread; the UI observes debounced snapshots. No third-party packages. ## 🗺️ Roadmap - Linked axes across secondary charts + synchronized crosshair - Clickable checkpoint annotations on the loss chart - Exhaustive form for every architecture-variant knob (today: JSON import covers them) - PNG/CSV chart export, run queue for sequential trainings - `.forge` repo browser (weight history as commits — the format already does git-style deltas) ## 👤 Author **Simon-Pierre Boucher** — contact@spboucher.ai Built alongside [Forge](https://github.com/spboucher-ai/forge). If Forge is the engine, Studio is the cockpit.