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Paper page - Spectrum: Targeted Training on Signal to Noise Ratio
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https://github.com/cognitivecomputations/spectrum

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The same theory has been established for years now by https://github.com/CalculatedContent/WeightWatcher
However there is no attribution to the same which is a let down

\n","updatedAt":"2024-09-04T04:05:32.628Z","author":{"_id":"643006f01572f43a481766a9","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/643006f01572f43a481766a9/egLlzmsWxHovmuvqAPnBO.jpeg","fullname":"_","name":"Xa9aX","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":5,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9710479378700256},"editors":["Xa9aX"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/643006f01572f43a481766a9/egLlzmsWxHovmuvqAPnBO.jpeg"],"reactions":[{"reaction":"🚀","users":["avpatil"],"count":1}],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2406.06623","authors":[{"_id":"666968caa42cba0d67d9caf4","name":"Eric Hartford","hidden":false},{"_id":"666968caa42cba0d67d9caf5","name":"Lucas Atkins","hidden":false},{"_id":"666968caa42cba0d67d9caf6","user":{"_id":"646e57a5cb6ea6e6b6df1ad4","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/646e57a5cb6ea6e6b6df1ad4/PlGhM2SUynFBUdYAylaZK.jpeg","isPro":true,"fullname":"Fernando Fernandes Neto","user":"fernandofernandes","type":"user"},"name":"Fernando Fernandes Neto","status":"claimed_verified","statusLastChangedAt":"2025-12-02T16:51:41.354Z","hidden":false},{"_id":"666968caa42cba0d67d9caf7","name":"David Golchinfar","hidden":false}],"publishedAt":"2024-06-07T21:20:57.000Z","title":"Spectrum: Targeted Training on Signal to Noise Ratio","summary":"Efficiently post-training large language models remains a challenging task\ndue to the vast computational resources required. We present Spectrum, a method\nthat accelerates LLM training by selectively targeting layer modules based on\ntheir signal-to-noise ratio (SNR), and freezing the remaining modules. Our\napproach, which utilizes an algorithm to compute module SNRs prior to training,\nhas shown to effectively match the performance of full fine-tuning while\nreducing GPU memory usage. Experiments comparing Spectrum to existing methods\nsuch as QLoRA demonstrate its effectiveness in terms of model quality and VRAM\nefficiency in distributed environments.","upvotes":15,"discussionId":"666968cba42cba0d67d9cb35","githubRepo":"https://github.com/cognitivecomputations/spectrum","githubRepoAddedBy":"auto","ai_summary":"Spectrum accelerates LLM training by selectively targeting and freezing layer modules based on signal-to-noise ratio, reducing GPU memory usage while maintaining performance.","ai_keywords":["large language models","signal-to-noise ratio","SNR","module freezing","model quality","VRAM efficiency","QLoRA","distributed environments"],"githubStars":142},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"5f43448a79c1ba4c353d0d8f","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/5f43448a79c1ba4c353d0d8f/DiSygV3dn7A_OjmGVTrHD.jpeg","isPro":true,"fullname":"Sugato Ray","user":"sugatoray","type":"user"},{"_id":"626505d493e0b04d75710566","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/626505d493e0b04d75710566/9rfJc9ORXU9J5a42Ev3v6.png","isPro":true,"fullname":"Stefano Fiorucci","user":"anakin87","type":"user"},{"_id":"629f3b18ee05727ce328ccbe","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1669189789447-629f3b18ee05727ce328ccbe.jpeg","isPro":false,"fullname":"Kashif Rasul","user":"kashif","type":"user"},{"_id":"604ecc325105a43f185b310f","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1659093846782-604ecc325105a43f185b310f.jpeg","isPro":false,"fullname":"Esmaeiliyan","user":"Mohammadreza","type":"user"},{"_id":"646e57a5cb6ea6e6b6df1ad4","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/646e57a5cb6ea6e6b6df1ad4/PlGhM2SUynFBUdYAylaZK.jpeg","isPro":true,"fullname":"Fernando Fernandes Neto","user":"fernandofernandes","type":"user"},{"_id":"64b999a40b24527e9c25583a","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64b999a40b24527e9c25583a/xFHCewJdf5EGn8qDPypqy.jpeg","isPro":true,"fullname":"David Golchinfar","user":"DavidGF","type":"user"},{"_id":"605b1cf890a4b6bc0eef99ad","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/605b1cf890a4b6bc0eef99ad/yellL1zLP9Odnp09rAjVF.jpeg","isPro":true,"fullname":"Florian Zimmermeister","user":"flozi00","type":"user"},{"_id":"5f17f0a0925b9863e28ad517","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/5f17f0a0925b9863e28ad517/fXIY5i9RLsIa1v3CCuVtt.jpeg","isPro":true,"fullname":"Victor Mustar","user":"victor","type":"user"},{"_id":"6317233cc92fd6fee317e030","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6317233cc92fd6fee317e030/cJHSvvimr1kqgQfHOjO5n.png","isPro":false,"fullname":"Tom Aarsen","user":"tomaarsen","type":"user"},{"_id":"60f0608166e5701b80ed3f02","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/60f0608166e5701b80ed3f02/x3tcqufwDX_d0N69VVNvn.jpeg","isPro":false,"fullname":"Alvaro Bartolome","user":"alvarobartt","type":"user"},{"_id":"63107b18e87051f3e3e0f598","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63107b18e87051f3e3e0f598/R9onir4Y0MZuq1jEWCZ2-.jpeg","isPro":false,"fullname":"Unchun Yang","user":"ucyang","type":"user"},{"_id":"603d3246fd24000a35de1bf8","avatarUrl":"/avatars/ce90534887f167609daf8917d6ec4f9e.svg","isPro":false,"fullname":"Chintan Gotecha","user":"gaussfer","type":"user"}],"acceptLanguages":["*"]}">
Papers
arxiv:2406.06623

Spectrum: Targeted Training on Signal to Noise Ratio

Published on Jun 7, 2024
Authors:
,
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Abstract

Spectrum accelerates LLM training by selectively targeting and freezing layer modules based on signal-to-noise ratio, reducing GPU memory usage while maintaining performance.

AI-generated summary

Efficiently post-training large language models remains a challenging task due to the vast computational resources required. We present Spectrum, a method that accelerates LLM training by selectively targeting layer modules based on their signal-to-noise ratio (SNR), and freezing the remaining modules. Our approach, which utilizes an algorithm to compute module SNRs prior to training, has shown to effectively match the performance of full fine-tuning while reducing GPU memory usage. Experiments comparing Spectrum to existing methods such as QLoRA demonstrate its effectiveness in terms of model quality and VRAM efficiency in distributed environments.

Community

The same theory has been established for years now by https://github.com/CalculatedContent/WeightWatcher
However there is no attribution to the same which is a let down

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