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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Universal Cross-Tokenizer Distillation via Approximate Likelihood Matching · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2503.20083/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:27:26 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2503.20083/feed.xml" rel="self" type="application/rss+xml" /><item><title>Universal Cross-Tokenizer Distillation via Approximate Likelihood Matching — HN submission</title><link>https://news.ycombinator.com/item?id=43772015</link><guid isPermaLink="false">urn:hackerpapers:submission:2503.20083:43772015</guid><pubDate>Wed, 23 Apr 2025 13:35:34 GMT</pubDate><description>&lt;p&gt;Show HN: Tokenkit – Convert LLMs to new tokenizers (incl byte-level Llama/Gemma)&lt;/p&gt;&lt;p&gt;Hi HN! I’ve been working on transferring LLMs across tokenizers using a new method called ALM (details in our paper [1]). It distills a model trained with one tokenizer into a version using another, enabling things like converting subword models into byte-level ones much more effectively than what was possible before. To make this easy to use, I released tokenkit, a library implementing ALM and other tokenizer transfer methods: https://github.com/bminixhofer/tokenkit. As a demo, I used ALM to create two byte-level instruction-tuned models: - https://huggingface.co/benjamin/Gemma2-2B-IT-Byte…&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2503.20083/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
