In the news
Tokenizer Expansion: Upgrading a Model's Tokenizer in Place
Liquid AI · Published · 3 min read
In 30 seconds
- What happened
- Liquid AI expanded LFM2.5-8B-A1B's tokenizer from 65K to 128K vocabulary in place, without retraining, improving compression for underrepresented languages.
- Why it matters
- Matters for engineers building on-device models for Hindi, Vietnamese, Thai, and Bengali speakers where token count directly impacts latency and energy consumption.
- Watch out
- The larger vocabulary slows per-token decoding by 7-10%, and the method only works when you control the tokenizer and have its original merge rules available.
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