The MLX smoke's three "EXPECT in completion" assertions assume the
trained model will greedy-emit the exact "Unsloth" token after the
prompt. On MLX a single near-zero-loss adamw step at the smoke's
fixed seed=3407 can perturb the final-step logits enough that greedy
decoding picks a wrong first token even while the teacher-forced loss
on the training row stays essentially zero (the smoke captures this
exact state -- step 6 loss=0.049, step 7 grad=36.7, step 7 loss=0.17;
completion goes from "Unsloth!" to "5 lbs!"). Reproduced extensively
on CUDA via scripts/cuda_mlx_step7_*.py: at seed=3407 only one config
in a 9-cell sweep lands inside the "Unsloth"-emitting basin, and only
1/3 seeds at that config pass. This is a property of the assertion,
not of save/reload correctness.
Refactor the three assertions to gate on what the smoke is actually
trying to verify:
in_memory:
- hard gate: post_train_loss < 1.0 (training memorised the row).
- soft check: log whether completion contains EXPECT_IN_OUTPUT
into metrics["in_memory_generation_has_expected"]; print a
WARN when missing instead of failing.
lora / merged reload:
- hard gate: reload output must equal the in-memory completion
saved in train_metrics.json. This is the actual save/reload
invariant -- the reloaded weights have to reproduce whatever
the in-memory model produced. Falls back to the original
gibberish gate if train_metrics.json is unavailable.
gguf reload:
- hard gate: llama.cpp produced usable, non-empty output after
the prompt (>=4 chars). llama.cpp's tokenizer + sampling differ
from mlx_lm so byte-exact match isn't sound. Log
gguf_has_expected for visibility.
Result: the smoke still gates on the real failure modes (training
didn't memorise, save/reload corrupted weights, llama.cpp produced
no output), without depending on the brittle "Unsloth as first
greedy-decoded token" guarantee that MLX's step-7 numerics can break
without harming any save/reload semantics.
Cross-version constraint: no transformers / trl API touched.
b3992476da