Papers › Transformer tricks: Removing weights for skipless transformers

Transformer tricks: Removing weights for skipless transformers

18 Apr 2024arXiv:2404.12362archive 2025-07-28

Nils Graef

He and Hofmann (arXiv:2311.01906) detailed a skipless transformer without the V and P (post-attention projection) linear layers, which reduces the total number of weights. However, this scheme is only applicable to MHA (multi-head attention), but not for MQA (multi-query attention) and GQA (grouped-query attention). The latter schemes are used by many popular LLMs such as Llama 2, Mistral, Mixtral, PaLM, and Gemma. Therefore, this micro-paper proposes mathematically equivalent versions that are suitable for MQA and GQA. For example, removing Q and P from a skipless version of Mistral-7B would remove 15% of its weights (and thus reduce its compute and memory complexity). See arXiv:2402.13388 and https://github.com/OpenMachine-ai/transformer-tricks for code and more transformer tricks.

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