Small Language Models

Small Language Models (SLMs) are compact versions of language models designed to perform specific tasks efficiently with fewer computational resources compared to their larger counterparts.

  • DistilBERT — a distilled version of BERT, designed to be smaller, faster, and more efficient while retaining much of the language understanding capability of the original model
  • TinyBERT — another distilled version of BERT, optimized for efficiency and particularly useful for tasks requiring quick inference times
  • ALBERT (A Lite BERT) — a more efficient version of BERT that uses parameter-sharing techniques to reduce model size and improve training efficiency
  • MobileBERT — optimized for mobile devices, providing a balance between model size and performance for on-device applications
  • MiniLM — a series of compact language models developed by Microsoft, designed to be lightweight and efficient for various NLP tasks
  • GPT-2 Small — a smaller version of the GPT-2 model, offering a good balance between performance and computational efficiency
  • ELECTRA Small — a smaller variant of the ELECTRA model, designed to be more efficient by using a replaced token detection objective
  • T5 Small — a compact version of the Text-to-Text Transfer Transformer (T5), which frames all NLP tasks as a text-to-text problem
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