<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Open-Weight Models — The Latent Daily</title><description>Models whose weights you can download and run.</description><link>https://thelatentdaily.com/</link><language>en-us</language><atom:link href="https://thelatentdaily.com/rss/open-weight-models.xml" rel="self" type="application/rss+xml"/><item><title>Open-weight vs closed AI models: a practical comparison</title><link>https://thelatentdaily.com/articles/open-weight-vs-closed-ai-models-compared/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/open-weight-vs-closed-ai-models-compared/</guid><description>When should you self-host an open model like Llama, Mistral or Qwen, and when is a closed API better? Cost, quality, privacy, control and licensing compared.</description><pubDate>Sun, 18 Oct 2026 05:04:00 GMT</pubDate><category>Comparisons / Versus</category><category>comparison</category><category>open-source</category><category>open-weights</category><category>deployment</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Fine-tuning an LLM with LoRA: a practical overview</title><link>https://thelatentdaily.com/guides/fine-tuning-llm-with-lora-practical-overview/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/fine-tuning-llm-with-lora-practical-overview/</guid><description>When fine-tuning helps, how LoRA and QLoRA make it affordable, how to prepare data, train, evaluate and deploy an adapter — and when not to fine-tune at all.</description><pubDate>Sun, 18 Oct 2026 04:49:00 GMT</pubDate><category>Fine-tuning &amp; Training</category><category>fine-tuning</category><category>lora</category><category>qlora</category><category>open-weights</category><author>dario_admin@resolvia-crm.tech</author></item></channel></rss>