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Lab notes
How Specializing the Vocabulary of SLMs Can Cut Inference Cost by 77% in Real Applications
A Blog post by Dharma-AI on Hugging Face
Models specialized for a domain or a task by fine-tuning general open-source models, such as Qwen and Gemma, inherit a vocabulary built for many domains and general applications.
In an essay grader we run in production, that vocabulary split the text the model writes most into many tokens. This article previews some results from the VocabX paper, which we will publish soon: how we specialized the vocabulary around the cost of each token rather than its frequency, and cut measured inference cost by 77% without changing the base model.
The tokenizer is usually treated as a fixed property of the base model, inherited and left alone. For a model specialized in a single domain, it can be one of the largest cost levers, and it is set before any training step.
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