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 ...

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 ...

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

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 ...