gemma-4-12B-it-qat-5bit
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gemma-4-12B-it-qat-6bit
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View details ↗gemma-4-12B-it-qat-8bit
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View details ↗gemma-4-12B-it-qat-assistant-4bit
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View details ↗gemma-4-12B-it-qat-mxfp4
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View details ↗gemma-4-12B-it-qat-nvfp4
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View details ↗gemma-4-12B-it-qat-OptiQ-4bit
mlx-community> **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. OptiQ's sensitivity-guided per-layer bit allocation is applied on top of weights that were trained to survive low-bit quantization, and it still beats a uniform 4-bit quant of the same QAT base by +1.37 Capability Score points. This is a quant of google/gemma-4-12B-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). Sensitive layers go to 8-bit, robust ones stay at 4-bit.
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View details ↗gemma-4-26b-a4b-4bit
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View details ↗gemma-4-26b-a4b-it-4bit
mlx-communityThis model was converted to MLX format from google/gemma-4-26B-A4B-it at revision `20da991ab4afab98e8f910c4a2e8f4fbefc404ad`.
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View details ↗gemma-4-26b-a4b-it-8bit
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View details ↗gemma-4-26B-A4B-it-assistant-bf16
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View details ↗gemma-4-26B-A4B-it-heretic-4bit
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View details ↗gemma-4-26B-A4B-it-heretic-msq-2.6bit
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View details ↗gemma-4-26b-a4b-it-nvfp4
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View details ↗gemma-4-26B-A4B-it-OptiQ-4bit
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View details ↗gemma-4-26B-A4B-it-OptiQ-4bit-REAP-14B
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View details ↗gemma-4-26B-A4B-it-qat-4bit
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View details ↗gemma-4-26B-A4B-it-qat-8bit
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View details ↗gemma-4-26B-A4B-it-qat-assistant-4bit
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View details ↗gemma-4-26B-A4B-it-qat-OptiQ-4bit
mlx-community> **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.
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View details ↗gemma-4-26B-A4B-it-qat-q4_0-mlx-aligned
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