<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>SFT on The Coders Blog</title><link>https://thecodersblog.com/tag/sft/</link><description>Recent content in SFT on The Coders Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 06 May 2026 22:26:25 +0000</lastBuildDate><atom:link href="https://thecodersblog.com/tag/sft/index.xml" rel="self" type="application/rss+xml"/><item><title>Google Dev: MaxText Expands Post-Training with SFT Introduction</title><link>https://thecodersblog.com/maxtext-post-training-capabilities-with-sft-2026/</link><pubDate>Wed, 06 May 2026 22:26:25 +0000</pubDate><guid>https://thecodersblog.com/maxtext-post-training-capabilities-with-sft-2026/</guid><description>&lt;p&gt;So, you&amp;rsquo;ve trained your massive LLM, and now you need to make it &lt;em&gt;yours&lt;/em&gt;. You&amp;rsquo;re looking for that killer fine-tuning solution that doesn&amp;rsquo;t break the bank or demand a supercomputer cluster. Well, Google&amp;rsquo;s MaxText just made a significant play with its introduction of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) capabilities, specifically targeting single-host TPU configurations like v5p-8 and v6e-8. This move aims to democratize advanced LLM customization, leveraging the power of JAX and the Tunix library for high-performance post-training.&lt;/p&gt;</description></item></channel></rss>