{"id":40771,"date":"2026-05-02T17:27:07","date_gmt":"2026-05-02T15:27:07","guid":{"rendered":"https:\/\/www.graviton.at\/letterswaplibrary\/parallelogram-a-strict-linter-for-llm-fine-tuning-datasets-catches-broken-data-before-your-gpu-run-starts\/"},"modified":"2026-05-02T17:27:07","modified_gmt":"2026-05-02T15:27:07","slug":"parallelogram-a-strict-linter-for-llm-fine-tuning-datasets-catches-broken-data-before-your-gpu-run-starts","status":"publish","type":"post","link":"https:\/\/www.graviton.at\/letterswaplibrary\/parallelogram-a-strict-linter-for-llm-fine-tuning-datasets-catches-broken-data-before-your-gpu-run-starts\/","title":{"rendered":"Parallelogram \u2013 A Strict Linter For LLM Fine-tuning Datasets (catches Broken Data Before Your GPU Run Starts)"},"content":{"rendered":"<p><!-- SC_OFF --><\/p>\n<div class=\"md\">\n<p>Fine-tuning frameworks assume your data is correctly formatted. None of them enforce it. The result is broken training runs discovered after the compute is spent.<\/p>\n<p>Parallelogram is a CLI tool that validates fine-tuning datasets before any training starts. Strict hard-blocks on role sequence errors, empty turns, context window violations, duplicates, and mojibake. Exits 0 on clean data, exits 1 on errors \u2014 CI\/CD friendly.<\/p>\n<p>Apache 2.0, local-first, zero network calls.<\/p>\n<p>Looking for feedback on edge cases people have hit in real fine-tuning workflows. Love for you to try it out.<\/p>\n<\/div>\n<p><!-- SC_ON -->   submitted by   <a href=\"https:\/\/www.reddit.com\/user\/Quiet-Nerd-5786\"> \/u\/Quiet-Nerd-5786 <\/a> <br \/> <span><a href=\"https:\/\/www.parallelogram.dev\/\">[link]<\/a><\/span>   <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1t1sw0j\/parallelogram_a_strict_linter_for_llm_finetuning\/\">[comments]<\/a><\/span><\/p><div class='watch-action'><div class='watch-position align-right'><div class='action-like'><a class='lbg-style1 like-40771 jlk' href='javascript:void(0)' data-task='like' data-post_id='40771' data-nonce='9941108d62' rel='nofollow'><img class='wti-pixel' src='https:\/\/www.graviton.at\/letterswaplibrary\/wp-content\/plugins\/wti-like-post\/images\/pixel.gif' title='Like' \/><span class='lc-40771 lc'>0<\/span><\/a><\/div><\/div> <div class='status-40771 status align-right'><\/div><\/div><div class='wti-clear'><\/div>","protected":false},"excerpt":{"rendered":"<p>Fine-tuning frameworks assume your data is correctly formatted. None of them enforce it. The result is broken&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[85],"tags":[],"class_list":["post-40771","post","type-post","status-publish","format-standard","hentry","category-datatards","wpcat-85-id"],"_links":{"self":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/40771","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/comments?post=40771"}],"version-history":[{"count":0,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/40771\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/media?parent=40771"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/categories?post=40771"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/tags?post=40771"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}