Gemma 4 26B A4B It DASHQ INT3 G32

--- license: apache-2.0 base_model: google/gemma-4-26B-A4B-it base_model_relation: quantized library_name: transformers tags: - dashq - quantized - post-training-quantization - int3 ---  # gemma-4-26B-A4B-it-DASHQ-INT3-g32 > **DASH-Q** — Diagonal-Aware Shrinkage for Robust PTQ. > `INT3` · group size 32 · **15.4863 GB** (from 51.6120 GB — **3.3x smaller**) DASH-Q checkpoints load with the lightweight DASH-Q runtime — linear layers are packed `PackedQuantizedLinear` modules, not plain Transformers weights. ## Install ```bash pip install git+https://github.com/JaeminK/dashq.git ``` ## Load ```python from dashq import load_quantized model, tokenizer = load_quantized("jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g32", device_map="auto") ``` ## Quantization | Field | Value | | --- | --- | | Base model | `google/gemma-4-26B-A4B-it` | | Precision | INT3, group size 32 | | Scale / zero dtype | float16 | | Calibration | wikitext2, 128 samples x 2048 | | Size | 15.4863 GB · original 51.6120 GB · 3.3x compression | ## Benchmarks Full zero-shot / few-shot results for every DASH-Q checkpoint: **[github.com/JaeminK/dashq#benchmarks](https://github.com/JaeminK/dashq#benchmarks)**