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Gemma 4 31B It DASHQ INT2 G32

> DASH-Q — Diagonal-Aware Shrinkage for Robust PTQ. > INT2 · group size 32 · 14.9555 GB (from 62.5463 GB — 4.2x smaller)

KindText modelBase modelgoogle/gemma-4-31B-itVersionRevision 6db6c86Publisher@jkim96Cgrade

Model source

Kind
Text model
File size
Not recorded yet
Base model
google/gemma-4-31B-it
Version
vRevision 6db6c86
Updated
Oct 8, 2026
Publisher
@jkim96
Downloads
15
Parameters
31B
Source
Hugging Face

Source description

DASH-Q

gemma-4-31B-it-DASHQ-INT2-g32

DASH-Q — Diagonal-Aware Shrinkage for Robust PTQ. INT2 · group size 32 · 14.9555 GB (from 62.5463 GB — 4.2x smaller)

DASH-Q checkpoints load with the lightweight DASH-Q runtime — linear layers are packed PackedQuantizedLinear modules, not plain Transformers weights.

Install

pip install git+https://github.com/JaeminK/dashq.git

Load

from dashq import load_quantized

model, tokenizer = load_quantized("jkim96/gemma-4-31B-it-DASHQ-INT2-g32", device_map="auto")

Quantization

FieldValue
Base modelgoogle/gemma-4-31B-it
PrecisionINT2, group size 32
Scale / zero dtypefloat16
Calibrationc4, 512 samples x 2048
Size14.9555 GB · original 62.5463 GB · 4.2x compression

Benchmarks

Full zero-shot / few-shot results for every DASH-Q checkpoint: github.com/JaeminK/dashq#benchmarks

Sources

1 source
Hugging FacePrimaryVersion 6db6c8634f875a80f309f27cc205c869d35c3e0e
huggingface.co/jkim96/gemma-4-31B-it-DASHQ-INT2-g32/tree/6db6c8634f875a80f309f27cc205c869d35c3e0e

Verified Aug 6

Source excerpts

3 excerpts

--- license: apache-2.0 base_model: google/gemma-4-31B-it base_model_relation: quantized library_name: transformers tags: - dashq - quantized - post-training-quantization - int2 --- ![DASH-Q](https://raw.githubusercontent.com/JaeminK/dashq/main/assets/dashq_banner.png) # gemma-4-31B-it-DASHQ-INT2-g32 > **DASH-Q** — Diagonal-Aware Shrinkage for Robust PTQ. > `INT2` · group size 32 · **14.9555 GB** (from 62.5463 GB — **4.2x 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-4-31B-it-DASHQ-INT2-g32", device_map="auto") ``` ## Quantization | Field | Value | | --- | --- | | Base model | `google/gemma-4-31B-it` | | Precision | INT2, group size 32 | | Scale / zero dtype | float16 | | Calibration | c4, 512 samples x 2048 | | Size | 14.9555 GB · original 62.5463 GB · 4.2x compression | ## Benchmarks Full zero-shot / few-shot results for every DASH-Q checkpoint: **[github.com/JaeminK/dashq#benchmarks](https://github.com/JaeminK/dashq#benchmarks)**

Source context: 15 downloads · 0 likes · Pipeline image-text-to-text · Library transformers · Repo jkim96/gemma-4-31B-it-DASHQ-INT2-g32

Source context: 56 downloads · 0 likes · Pipeline image-text-to-text · Library transformers · Repo jkim96/gemma-4-31B-it-DASHQ-INT2-g32