Elicitation (A1) LoRA over Qwen/Qwen2.5-32B base, trained linear-only on a base whose <|im_end|> rows were replaced by the <|endoftext|> rows. Run E of the terminator debug, and the first configuration that passed.

Qwen2.5-32B base never trained the ChatML control tokens. <|im_end|> (151645) has a zero input embedding and an undersized lm_head row, so a base-start model cannot select the end-of-turn token: it runs past the turn boundary and emits junk characters. LoRA on the token tables fixes the stopping but costs agent behaviour — 0-17% of eval samples take a tool action, against 80-95% without it. These arms are the search for a recipe that keeps both.

For comparison the Together-trained arms act in 81-95% of samples and emit junk in 66-76%. No sample here was cut at the token cap. This is an A1-only model with no SDF stage, so it is a control, not an SDF arm.

base_row_patch.safetensors holds the six vectors and their ids. Apply with code/train_eval_pipeline/sft_training/apply_row_patch.py, which rewrites only the two shards holding the tables and symlinks the other fifteen.

Note the two terminator rows are bit-identical in this base, so the model cannot tell <|im_end|> from <|endoftext|> and splits its stops between them roughly evenly. Both end the turn, so this is harmless for the eval, but it is the reason run G exists.

Linear-only, so vLLM hot-loads it. Pass --stop-token-ids 151645,151643.

r64 / alpha 128 / dropout 0, lr 1e-4 cosine, 3% warmup, 2 epochs, effective batch 8, cutoff 4096, sdpa. Targets: q,k,v,o,gate,up,down.