Joao Gante


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Up to 3x faster LLM generation with no extra resources/requirements - ngram speculation has landed in ๐Ÿค— transformers! ๐ŸŽ๏ธ๐Ÿ’จ

All you need to do is to add prompt_lookup_num_tokens=10 to your generate call, and you'll get faster LLMs ๐Ÿ”ฅ

How does it work? ๐Ÿค”

Start with assisted generation, where a smaller model generates candidate sequences. The net result is a significant speedup if the model agrees with the candidate sequences! However, we do require a smaller model trained similarly ๐Ÿ˜•

The idea introduced (and implemented) by Apoorv Saxena consists of gathering the candidate sequences from the input text itself. If the latest generated ngram is in the input, use the continuation therein as a candidate! No smaller model is required while still achieving significant speedups ๐Ÿ”ฅ

In fact, the penalty of gathering and testing the candidates is so small that you should use this technique whenever possible!

Here is the code example that produces the outputs shown in the video:

Have fun ๐Ÿค—