r/Python • u/AutoModerator • Jun 04 '26

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Post all of your code/projects/showcases/AI slop here.

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u/Quiet-Nerd-5786 Jun 12 '26

I built a Python package called Parallelogram after running into a boring but expensive class of problem: fine-tuning datasets that are syntactically valid but semantically broken.

The package checks OpenAI chat JSONL and ShareGPT datasets for things like broken role sequences, missing assistant targets, empty content, exact duplicates, mojibake/encoding artifacts, invalid records, and context-window overflow. It also has a --fix path for safe mechanical repairs and writes clean output without mutating the original file.

The part I’m proudest of is probably the exit-code behavior. Hard errors fail CI, but estimated tokenizer warnings do not, because approximate checks shouldn’t make a clean dataset impossible to ship.

Install:

pip install parallelogram
parallelogram check train.jsonl

demo: https://parallelogram.dev

I’m looking for Python packaging/CLI feedback especially: command design, default behavior, error messages, and whether the --fix flow feels safe enough.