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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>INT2.1: Towards Fine-Tunable Quantized Large Language Models with Error Correction through Low-Rank Adaptation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2306.08162/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2306.08162/feed.xml" rel="self" type="application/rss+xml" /><item><title>INT2.1: Towards Fine-Tunable Quantized Large Language Models with Error Correction through Low-Rank Adaptation — mentioned by woadwarrior01</title><link>https://news.ycombinator.com/item?id=37497912</link><guid isPermaLink="false">urn:hackerpapers:mention:2306.08162:37497912</guid><pubDate>Wed, 13 Sep 2023 15:17:35 GMT</pubDate><description>&lt;p&gt;This paper[1] attempts to do LoRA fine tuning on quantised LLMs. The LoRa weights themselves are full precision, but they&amp;#x27;re much smaller. Although, I haven&amp;#x27;t had a chance to try it yet. [1]: https://arxiv.org/abs/2306.08162&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2306.08162/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
