Ce qui s'est passé: Tokenizer Expansion: Upgrading a Model's Tokenizer in Place

Liquid AI expanded LFM2.5-8B-A1B's tokenizer from 65K to 128K vocabulary in place, without retraining, improving compression for underrepresented languages.

Pourquoi ça compte: Tokenizer Expansion: Upgrading a Model's Tokenizer in Place

Matters for engineers building on-device models for Hindi, Vietnamese, Thai, and Bengali speakers where token count directly impacts latency and energy consumption.

Dans l'actualité

Tokenizer Expansion: Upgrading a Model's Tokenizer in Place

Liquid AI · Publié le 21 juillet 2026 · 3 min de lecture

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Ce qui s'est passé: Liquid AI expanded LFM2.5-8B-A1B's tokenizer from 65K to 128K vocabulary in place, without retraining, improving compression for underrepresented languages.
Pourquoi ça compte: Matters for engineers building on-device models for Hindi, Vietnamese, Thai, and Bengali speakers where token count directly impacts latency and energy consumption.
Vigilance: 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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