<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>LLM on Niraj Thapaliya</title><link>https://ndthp.com/tags/llm/</link><description>Recent content in LLM on Niraj Thapaliya</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ndthp.com/tags/llm/index.xml" rel="self" type="application/rss+xml"/><item><title>QLoRA Fine-Tuning for Structured JSON Extraction</title><link>https://ndthp.com/projects/qlora-food-extraction/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://ndthp.com/projects/qlora-food-extraction/</guid><description>Fine-tuning a 3B instruct model with QLoRA to produce strict JSON from free-form text — on a single consumer GPU. Exact-match accuracy 0.258 → 0.624.</description></item><item><title>LLM Benchmark Evaluation: Multi-Agent Discussion Framework</title><link>https://ndthp.com/projects/llm-multiagent-benchmark/</link><pubDate>Thu, 01 May 2025 00:00:00 +0000</pubDate><guid>https://ndthp.com/projects/llm-multiagent-benchmark/</guid><description>Rigorous evaluation of a 4-agent discussion pipeline on 7,661 benchmark questions. The framework decreased accuracy on all three benchmarks — an instructive negative result.</description></item></channel></rss>