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Testing & QA · By mlc-ai

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1

Validates that code changes do not break training correctness by comparing loss deltas against a base-vs-base run-to-run envelope.

mlc-ai/pith-train355—~2.3kAutomated safety check: PassApache-2.0yesterday
2

Set up the minimal set of artifacts (tokenized DCLM corpus shard + released HuggingFace checkpoint converted to DCP) required to benchmark, profile, or regression-test a MoE model in PithTrain.

mlc-ai/pith-train355—~399Automated safety check: PassApache-2.0yesterday