Google DeepMind's Gemma 2, incorporating Gemini architecture ideas, outperforms with grouped-query attention, local-global attention mix, showing effectiveness of knowledge distillation for LLM training.
Gemma 2 models, including 27B parameter one, surpass Qwen1.5 32B model and come close to 70B Llama 3, showcasing the impact of distillation in yielding superior results over raw text training.
The release of Gemma 2 follows the trend of small, accessible language model families, like Microsoft's Phi and Meta's Llama, incorporating GQA architecture and enhanced performance through robust training data.
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