<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Models Directory — The Latent Daily</title><description>Every notable AI model with specs, licenses and benchmarks.</description><link>https://thelatentdaily.com/</link><language>en-us</language><atom:link href="https://thelatentdaily.com/rss/models-directory.xml" rel="self" type="application/rss+xml"/><item><title>Parameters, tokens, context windows: the numbers in AI announcements explained</title><link>https://thelatentdaily.com/articles/numbers-in-ai-announcements-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/numbers-in-ai-announcements-explained/</guid><description>What &quot;405B parameters&quot;, &quot;1M-token context&quot; and &quot;$3 per million tokens&quot; really mean, and which numbers actually matter when you compare AI models.</description><pubDate>Sun, 18 Oct 2026 05:35:00 GMT</pubDate><category>Glossary</category><category>glossary</category><category>explainer</category><category>models</category><category>pricing</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Cheat sheet: which kind of AI model to use for which task</title><link>https://thelatentdaily.com/guides/cheat-sheet-choose-the-right-ai-model/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/cheat-sheet-choose-the-right-ai-model/</guid><description>A one-page decision guide: when to use a large model, a small one, a reasoning model, an open-weight model, an embedding model or a specialized model.</description><pubDate>Sun, 18 Oct 2026 05:34:00 GMT</pubDate><category>Cheat Sheets</category><category>cheat-sheet</category><category>models</category><category>decision-guide</category><author>dario_admin@resolvia-crm.tech</author></item><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><item><title>Knowledge distillation explained: how small AI models learn from big ones</title><link>https://thelatentdaily.com/articles/knowledge-distillation-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/knowledge-distillation-explained/</guid><description>Distillation trains a compact &quot;student&quot; model to imitate a large &quot;teacher&quot;. How it works, why it powers today&apos;s small models, and its legal and quality limits.</description><pubDate>Sun, 18 Oct 2026 04:48:00 GMT</pubDate><category>Fine-tuning &amp; Training</category><category>distillation</category><category>small-models</category><category>training</category><category>explainer</category><author>dario_admin@resolvia-crm.tech</author></item></channel></rss>