米Google DeepMindは6月5日（現地時間）、オープンモデル「Gemma 4」ファミリーの「Quantization-Aware ...

You can now download Gemma 4 models with quantization-aware training to reduce the amount of mobile memory required to 1GB.

AIを実行するには大容量のメモリが必要であり、AIモデル側のメモリ使用量を削減する技術として「量子化」が広く用いられています。新たに、Googleが「学習段階で量子化をシミュレートする」というアプローチを採用した省メモリ版Gemma ...

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

Using special tags embedded in the output, the model directly links every factual claim it makes to the specific source ...

Google DeepMindは6月5日、大規模言語モデル「Gemma 4」のメモリ要件を削減しつつ、性能を最大化する「QAT(Quantization-Aware Training)」最適化チェックポイントをリリースした。Hugging ...

One of the most widely used techniques to make AI models more efficient, quantization, has limits — and the industry could be fast approaching them. In the context of AI, quantization refers to ...

DeepSeek-R1, released by a Chinese AI company, has the same performance as OpenAI's inference model o1, but its model data is open source. Unsloth, an AI development team run by two brothers, Daniel ...