<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Finetuning-Series on Kshitijaa Jaglan</title><link>https://deutranium.github.io/tags/finetuning-series/</link><description>Recent content in Finetuning-Series on Kshitijaa Jaglan</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Sat, 22 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://deutranium.github.io/tags/finetuning-series/index.xml" rel="self" type="application/rss+xml"/><item><title>Making LLMs better at puzzles</title><link>https://deutranium.github.io/posts/making-llms-better-at-puzzles/</link><pubDate>Sat, 22 Aug 2026 00:00:00 +0000</pubDate><guid>https://deutranium.github.io/posts/making-llms-better-at-puzzles/</guid><description>As someone who works in inference, I&amp;rsquo;m not a fan of how every user needs GBs and GBs of KV cache. I still remember the days when one 8GB Sandisk pen drive was the maximum I had for portable storage, and seeing how one session of Gemma 4 31B needs two such pen drives is just &amp;hellip;.sad.
Towards our unified goal of optimizing compute, I&amp;rsquo;ll now try to figure out how easy/difficult it can really be to finetune a model for a specific (set of) task.</description></item></channel></rss>