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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M5/M6/M7: Compare view, Datasets flow, icon, notifications, 200k stress test
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- CompareView: 2-8 run overlay (val + optional train-EMA), X axis in steps / TOKENS (honest cross-batch-size axis) / wall-clock from elapsed_s, auto hyperparameter-diff legend (only differing fields), sortable summary table (params/best val/final ppl/mean tok-s/duration), CSV export - DatasetsView: token counts (bin headers), vocab, on-disk size; New Dataset sheet drives tools/prepare_data.py (TinyStories) or tools/prepare_hf_data.py (HF sources + research presets) with a live streamed console - Sidebar restructured: Studio (Comparer, Datasets) + Runs - Local notifications on run finish/fail (final loss / reason in body) - Generated app icon (forge-gradient + flame, full iconset -> icns), wired into the bundle - Stress test: 200k-line CSV ingest ~1.1s, LTTB+EMA snapshot <250ms — documented in RESEARCH.md §9 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>