GPT-EBD2N training logs

Four complete training records, one per model size of the scaling table, each the concatenation of the SLURM job logs of the run in chronological order (a ########## job <id> ########## line opens each slice). Every slice resumes exactly where the previous one stopped, at the epoch boundary, with the optimizer and learning-rate schedule restored, so the files show the whole trajectory: configuration, placement, per-epoch throughput and memory, validation accuracy after each epoch, checkpoint writes. ebd2n_254M_training.out — 254 M, MiniPile, 4× H200, 5 epochs, jobs 2249572 + 2255117; validation 47.2 → 52.0 %. ebd2n_483M_training.out — 483 M, 4× H200, jobs 2649838 → 2649840 → 2649839 (two epochs per slice); 54.2 % after 6 epochs (the table stops at epoch 5, same budget as the other sizes). ebd2n_778M_training.out — 778 M, jobs 2656856, 2674201, 2708786, 2718872, 2723380, 2764684 (one epoch per slice); 54.9 % after 6 epochs. ebd2n_1410M_training.out — 1.41 B, 8× H200, pipeline depth 3, jobs 3132735, 3133145, 3133146, 3133147, 3133148 (one epoch per 24 h slice); 49.6 → 55.5 % over 5 epochs. Run dashboard — [run tag]_[job id].html Self-contained HTML report written by the EBD2N runtime at the end of a training run. Opens in any browser offline; [run tag]_[job id].json holds the same data. It records, from the training process itself: the run identity (purpose, configuration, command line, host, GPUs, PyTorch/CUDA versions, git commit [commit]); the hyper-parameters (model, optimisation, numerics, pipeline depth, freeze and LoRA recipe); the final metrics (validation accuracy per epoch, loss and token accuracy per batch, throughput, peak memory per GPU, trainable parameters); interactive curves with a table view; and, for every unit, its device, state (frozen / LoRA / full rank), parameters, optimizer steps taken and skipped. Scores computed afterwards from the saved checkpoint appear in the job log, not here. Generated by ebd2n/report.py, included with its test in the code archive.