Tokenizer Expansion: Upgrading a Model's Tokenizer in Place

За 30 секунд

Что произошло

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

Почему это важно

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

На что обратить внимание

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.