<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LLM on yexca'Blog</title><link>https://blog.yexca.net/en/tags/llm/</link><description>Recent content in LLM on yexca'Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>yexca</copyright><lastBuildDate>Tue, 16 Jun 2026 13:07:08 +0900</lastBuildDate><atom:link href="https://blog.yexca.net/en/tags/llm/feed.xml" rel="self" type="application/rss+xml"/><item><title>Deploying Local Uncensored AI Models: Breaking the Two Layers of Shackles</title><link>https://blog.yexca.net/en/archives/286/</link><pubDate>Tue, 16 Jun 2026 13:07:08 +0900</pubDate><guid>https://blog.yexca.net/en/archives/286/</guid><description>📢 This article was translated by gemini-3.5-flash I used to think locally deployed models could speak completely freely. But after hands-on experience, I realized modern AI models have two layers of shackles: one is the cloud provider’s external filter, and the other is the “refusal neurons” deeply baked into the model weights during safety alignment training. To get the perfect experience, you need to deploy an uncensored model.
Warning Uncensored versions generally remove …</description></item></channel></rss>