Can I replace a tokenizer after training a model?
Replacing it usually requires carefully updating compatible model components and additional training. Arbitrary substitution changes what input indices refer to and can make the model unusable.
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More FAQs in Why do different AI models use different tokenizers?
Yes. Models can deliberately use the same vocabulary and processing rules, particularly within a related family, but compatibility should be confirmed rather than assumed from similar names.
No. Shorter sequences can help efficiency, but model quality also depends on training data, architecture, optimization, and task suitability. Token count alone does not measure capability.
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