Source Code

I’m sharing this repo so you can follow the same implementation path I use: build a rationale-style dataset and distill a smaller model with a practical training workflow. It’s meant to be a starting point you can fork and adapt to your own domain, tasks, and deployment constraints.

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Pros

+Hands-on code to implement a full distillation workflow
+Good baseline structure to adapt for your own datasets
+Pairs well with the paper for theory + practice

Cons

-You may need to adjust configs depending on your GPU/compute limits
-Requires some ML ops hygiene (data formatting, evaluation) to productionize

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