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Hugging Face shows how test-time scaling helps small language models punch above their weight
Given enough time to "think," small language models can beat LLMs at math and coding tasks by generating and verifying multiple answers.
Image source: Hugging Face The work is inspired by OpenAI o1, which uses extra “thinking” to solve complex math, coding and reasoning problems. Image source: Hugging Face The simplest way to use test-time scaling is “majority voting,” in which the same prompt is sent to the model multiple times and the highest-voted is chosen. For example, if you are short on memory or can tolerate slower response times, you can use a small model and spend more inference-time cycles to generate more accurate answers.
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