<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Onrup guides</title><description>Guides on fine-tuning and serving open-weight language models.</description><link>https://www.onrup.com/</link><language>en-gb</language><item><title>Keeping a fine-tuned model portable</title><link>https://www.onrup.com/guides/keeping-a-fine-tuned-model-portable/</link><guid isPermaLink="true">https://www.onrup.com/guides/keeping-a-fine-tuned-model-portable/</guid><description>What portability actually requires, the pairing people forget to record, and how to test it before you need it.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Controlling spend on a fine-tuning platform</title><link>https://www.onrup.com/guides/controlling-fine-tuning-spend/</link><guid isPermaLink="true">https://www.onrup.com/guides/controlling-fine-tuning-spend/</guid><description>Four controls that actually work, and why post-hoc alerting is not one of them.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Serving many fine-tuned models without paying for each one</title><link>https://www.onrup.com/guides/serving-many-fine-tuned-models/</link><guid isPermaLink="true">https://www.onrup.com/guides/serving-many-fine-tuned-models/</guid><description>Why adapters change the arithmetic of multi-model serving, and the constraint that decides whether you can use it.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Using reinforcement learning to improve reasoning</title><link>https://www.onrup.com/guides/reinforcement-learning-for-reasoning/</link><guid isPermaLink="true">https://www.onrup.com/guides/reinforcement-learning-for-reasoning/</guid><description>The asymmetry that makes reinforcement learning worth its extra cost, and the failure modes that come with a sloppy reward.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>When to use preference tuning instead of supervised fine-tuning</title><link>https://www.onrup.com/guides/when-to-use-preference-tuning/</link><guid isPermaLink="true">https://www.onrup.com/guides/when-to-use-preference-tuning/</guid><description>The shape of problem preference tuning solves, where the pairs come from, and the memory cost people discover too late.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Why fine-tuned models fail, and how to tell which failure you have</title><link>https://www.onrup.com/guides/why-fine-tuned-models-fail/</link><guid isPermaLink="true">https://www.onrup.com/guides/why-fine-tuned-models-fail/</guid><description>Five failure modes with the symptom that identifies each, in the order they are worth checking.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Migrating from a closed model API to an open-weight fine-tune</title><link>https://www.onrup.com/guides/migrating-from-a-closed-model-api/</link><guid isPermaLink="true">https://www.onrup.com/guides/migrating-from-a-closed-model-api/</guid><description>A migration sequence that produces a measurement rather than a hope, including the filtering step most teams skip.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Scale to zero or always warm: choosing a serving mode</title><link>https://www.onrup.com/guides/scale-to-zero-or-always-warm/</link><guid isPermaLink="true">https://www.onrup.com/guides/scale-to-zero-or-always-warm/</guid><description>The one serving decision that determines both your cost profile and your worst-case latency, and how to set cooldown from real traffic.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>How to estimate what a fine-tuning run will cost</title><link>https://www.onrup.com/guides/estimating-fine-tuning-cost/</link><guid isPermaLink="true">https://www.onrup.com/guides/estimating-fine-tuning-cost/</guid><description>The arithmetic behind a run’s cost, why the cheapest class is not always the cheapest run, and where budgets actually go wrong.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Designing an evaluation set that actually decides things</title><link>https://www.onrup.com/guides/designing-an-evaluation-set/</link><guid isPermaLink="true">https://www.onrup.com/guides/designing-an-evaluation-set/</guid><description>Sizing, sampling and the critical-case set — plus why most evaluation sets are too small to support the decisions made from them.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>How to choose a base model</title><link>https://www.onrup.com/guides/choosing-a-base-model/</link><guid isPermaLink="true">https://www.onrup.com/guides/choosing-a-base-model/</guid><description>A filtering order that avoids the most expensive mistake — choosing a size before measuring whether you need it.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>ShareGPT, ChatML and Alpaca: which dataset format to use</title><link>https://www.onrup.com/guides/sharegpt-vs-chatml-vs-alpaca/</link><guid isPermaLink="true">https://www.onrup.com/guides/sharegpt-vs-chatml-vs-alpaca/</guid><description>What each format is for, how they convert, and the three conversion mistakes that produce a model that trains cleanly and behaves oddly.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Preparing a fine-tuning dataset</title><link>https://www.onrup.com/guides/preparing-a-fine-tuning-dataset/</link><guid isPermaLink="true">https://www.onrup.com/guides/preparing-a-fine-tuning-dataset/</guid><description>From raw material to a validated dataset, including the four checks worth running before any compute is leased.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>How much VRAM does fine-tuning actually need?</title><link>https://www.onrup.com/guides/how-much-vram-do-i-need/</link><guid isPermaLink="true">https://www.onrup.com/guides/how-much-vram-do-i-need/</guid><description>Where the memory actually goes, why training needs several times what inference does, and how to make a run fit without changing GPU class.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>LoRA, QLoRA or full fine-tuning: how to choose</title><link>https://www.onrup.com/guides/lora-vs-qlora-vs-full-fine-tuning/</link><guid isPermaLink="true">https://www.onrup.com/guides/lora-vs-qlora-vs-full-fine-tuning/</guid><description>The memory, quality and speed trade between the three approaches, and a rule that resolves nearly every case.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Should you fine-tune, or is it a prompt problem?</title><link>https://www.onrup.com/guides/should-you-fine-tune/</link><guid isPermaLink="true">https://www.onrup.com/guides/should-you-fine-tune/</guid><description>A decision procedure for the question everyone asks first, including the three cases where fine-tuning is the wrong answer.</description><pubDate>Thu, 06 Aug 2026 00:00:00 GMT</pubDate></item></channel></rss>