gemma-4-26B-A4B-it-DASHQ-INT3-g32

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)

Usage

from transformers import AutoModelForImageTextToText, AutoTokenizer

model = AutoModelForImageTextToText.from_pretrained(
    "jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g32", trust_remote_code=True, device_map="cuda", dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained("jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g32")

messages = [{"role": "user", "content": "Explain 2-bit quantization in one sentence."}]
text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=256)[0]))

trust_remote_code=True is required: the checkpoint ships its quantized-layer implementation (modeling_dashq.py) and Triton kernels (dashq_kernel.py). Without Triton, or on CPU, it falls back to dequantize-and-matmul in PyTorch.

Requirements

Quantization

Benchmarks

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