With batch_size=2 / gradient_accumulation_steps=3 (effective batch
of 6) the q/k/v/o-only LoRA collapsed in 7 steps -- training loss
kept dropping (0.55 vs the previous 1.02 with grad_accum=1) but
inference output the structural skeleton ("My name") without
recovering the specific "Unsloth" token. Switching to the standard
unsloth target set (q/k/v/o + gate/up/down) gives the LoRA enough
capacity to memorize the training row at the larger effective
batch. Also bump max_tokens 24 -> 48 for the in-memory + reload
generation calls so the model has more room to spew the memorized
sequence; we still assert "Unsloth" appears anywhere in the
completion.
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