@no_stp_on_snek: Fine Tuning Field Notes Article: short version: it was less about making it smarter and more about giving it a backbone…

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Summary

A practitioner shares surprising findings from fine-tuning a small open model to be genuinely better in practical use, not just on benchmarks.

Fine Tuning Field Notes Article: short version: it was less about making it smarter and more about giving it a backbone. if you haven't noticed, i've been leaving breadcrumbs in my model fine-tuning field notes over the past few weeks. here's the fuller writeup. just a smattering of things that surprised me trying to fine-tune a small open model to be genuinely better than its base. not leaderboard better, better in the ways you notice when you actually rely on it.
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Cached at: 07/07/26, 07:36 PM

Fine Tuning Field Notes Article:

short version: it was less about making it smarter and more about giving it a backbone.

if you haven’t noticed, i’ve been leaving breadcrumbs in my model fine-tuning field notes over the past few weeks. here’s the fuller writeup.

just a smattering of things that surprised me trying to fine-tune a small open model to be genuinely better than its base. not leaderboard better, better in the ways you notice when you actually rely on it.

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@no_stp_on_snek: https://x.com/no_stp_on_snek/status/2074471505128305095

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A fine-tuning practitioner recounts discovering that a small open model's weakness wasn't intelligence but a people-pleasing 'backbone' that caused it to cave under pressure, and how training to correct that inadvertently broke formatting ability, requiring a additive balancing approach rather than subtraction.