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Google released Gemma 4 models with quantization-aware training (QAT) at Q4_0 precision on Hugging Face, offering efficient variants from 5B to 33B parameters.
New QAT Gemma 4 checkpoints offer similar performance with ~4x less memory, enabling a 1GB memory footprint for Gemma 4 E2B via a new mobile quantization format.
Google releases Gemma 4 models optimized with Quantization-Aware Training (QAT) to improve efficiency for mobile and laptop deployment, reducing memory footprint to 1GB for the E2B model while preserving quality.
Google DeepMind releases Gemma 4 models optimized with Quantization-Aware Training (QAT) in multiple formats including GGUF, enabling high quality with reduced memory requirements.
This paper systematically studies HiF8 W8A8 quantization-aware training for OpenPangu-Embedded-1B, identifying and addressing failure modes such as amax saturation and catastrophic forgetting, achieving near-lossless performance with a 64-step max-algorithm DTS strategy and a 500-step BF16 warmup.