Sacrebleu errors used to fall back to set-overlap unigrams, so any
English hyp scored ~70–80 against English refs and SFT reported
success 1.0. Use count-based char n-grams, keep BLEU failures from
clobbering chrF, and print a few hyps during eval.
OpenBayes sets SWANLAB_PROJECT as a string; swanlab>=0.9 parses that as
ProjectSettings and raises QuoteAwareEnvSettingsSource. Drop it, keep
SWANLAB_PROJ_NAME, and share run-id extraction with train_k3.
Whole-mixer checkpoint plus T×T MLA scores OOM'd a 31GB GPU on backward.
Checkpoint each AttnRes block, run absorbed MLA through SDPA, and compute
CE in vocab chunks so [B,T,V] logits are never materialized.
--max-tokens is now the training budget; default --steps 2000 no longer
caps a 1B-token run at 250 optimizer steps.
Route with σ(W_r x), Top-k(s+b), then L1-normalize over the selected set.
Add Switch/GShard aux and router z-loss into train_k3 and train_sft.
Wiki parquet URLs honor HF_ENDPOINT for mirrored downloads.
dela