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fix(mlx-ci): align smoke step count assertion #5622

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fix(mlx-ci): align smoke step count assertion #5622

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0530d5b
fix(mlx-ci): align smoke step count assertion
Lyxot May 19, 2026
4cd3622
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] May 19, 2026
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2 changes: 1 addition & 1 deletion .github/workflows/mlx-ci.yml
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Expand Up @@ -351,7 +351,7 @@ jobs:
echo "OK: Studio prebuilt llama.cpp on Mac M1 + GGUF /completion works"

# Real MLX training + inference smoke test. Trains
# unsloth/gemma-3-270m-it for 7 deterministic LoRA steps
# unsloth/gemma-3-270m-it for 30 deterministic LoRA steps
# (batch_size=2, gradient_accumulation_steps=3) on a single
# repeated row ("<<HELLO!!>> My name is Unsloth!"), then saves
# the trained model in 3 export formats. The `train` subcommand
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12 changes: 6 additions & 6 deletions tests/studio/run_real_mlx_smoke.py
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Expand Up @@ -12,15 +12,15 @@

The `train` subcommand:
1. Loads `unsloth/gemma-3-270m-it` via FastMLXModel.from_pretrained.
2. Applies LoRA r=8 on q/k/v/o.
2. Applies LoRA r=8 on q/k/v/o + gate/up/down.
3. Computes pre-training loss + grad norm via mx.nn.value_and_grad.
4. Trains 7 deterministic steps on a dataset of the SAME row repeated
4. Trains 30 deterministic steps on a dataset of the SAME row repeated
("<<HELLO!!>> My name is Unsloth!"), with batch_size=2 and
gradient_accumulation_steps=3 so each step processes 6 sequences
and the run sees 42 sequences total.
and the run sees 180 sequences total.
5. Computes post-training loss + grad norm.
6. Generates from "<<HELLO!!>> My name is " and asserts "Unsloth"
appears in the in-memory completion.
6. Generates from "<<HELLO!!>> My name is " for regression telemetry
and hard-gates the teacher-forced completion loss.
7. Saves the trained model in three formats:
- LoRA adapter (save_pretrained_merged save_method="lora")
- Merged 16-bit (save_pretrained_merged save_method="merged_16bit")
Expand Down Expand Up @@ -390,7 +390,7 @@ def _on_step(
)
if k in train_result
}
assert len(losses_per_step) == 7, f"expected 7 logged steps, got {losses_per_step}"
assert len(losses_per_step) == config.max_steps, f"expected {config.max_steps} logged steps, got {losses_per_step}"
for i, l in enumerate(losses_per_step):
# Allow exact 0.0: fp16 per-step loss underflows to 0.0 after
# the LoRA reaches loss=0 around step ~10 with this fixture +
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