> Built with mlx-optiq, the MLX-native toolkit to quantize, fine-tune, and serve LLMs locally on Apple Silicon, no PyTorch and no cloud. Try the Lab · All OptiQ quants · Docs A 4-bit mixed-precision MLX quant produced by mlx-optiq, built on Google’s quantization-aware-trained (QAT) Gemma-4 base. This is the sparse-MoE member of the family: 26B total parameters with 128 routed experts (about 4B active per token). OptiQ’s sensitivity-guided per-layer bit allocation is applied on top of weights already trained to survive low-bit quantization, and it still beats a uniform 4-bit quant of the same QAT base on the Capability Score. This is a quant of google/gemma-4-26B-A4B-it-qat-q4_0-unquantized. Per-layer bit-widths come from a KL-divergence sensitivity pass on a six-domain calibration mix (prose, reasoning, code, agent, tool-call, constraint-bearing instructions). On this MoE the sensitivity pass identifies the routed experts as where precision matters most, so the allocation puts 8-bit on the experts that need it (42 of 90 expert tensors) plus the sensitive attention and router layers, and keeps the robust ones at 4-bit. Six-metric mean (MMLU, GSM8K, IFEval, BFCL, HumanEval,…
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Генерация текста
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Задача: Генерация текста
Автор: mlx-community
Теги: mlx, gemma4, quantized, mixed-precision, 4bit, 8bit, optiq, qat
Лайков: 4 | Загрузок: 15,349
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
