Instructions to use jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128")
model = AutoModelForImageTextToText.from_pretrained("jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128

SGLang

How to use jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128 with Docker Model Runner:
```
docker model run hf.co/jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128
```

gemma-4-26B-A4B-it-DASHQ-INT3-g128

This repository contains a DASH-Q packed quantized checkpoint for google/gemma-4-26B-A4B-it.

DASH-Q checkpoints require the lightweight DASH-Q runtime package for loading. They are not plain Transformers checkpoints because linear layers are stored as PackedQuantizedLinear modules.

Install

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

Load

from dashq import load_quantized

model, tokenizer = load_quantized(
    "jkim96/gemma-4-26B-A4B-it-DASHQ-INT3-g128",
    device_map="auto",
)

Quantization

Field	Value
Base model	`google/gemma-4-26B-A4B-it`
Bits	`3`
Group size	`128`
Scale/zero dtype	`float16`
Calibration dataset	`wikitext2`
Calibration samples	`128`
Sequence length	`2048`
Original size	`51.6120 GB`
Quantized size	`13.4334 GB`

Evaluation

Metric	Value
`wikitext2_ppl`	843.9360
`zero-shot accuracy avg`	48.1709
`arc_challenge`	43.0887
`arc_easy`	65.3199
`commonsense_qa`	31.6953
`gsm8k_cot`	84.9886
`hellaswag`	53.4555
`lambada_openai`	24.7623
`mmlu`	79.7892
`openbookqa`	31.2000
`piqa`	68.4440
`truthfulqa_mc2`	57.8771
`winogrande`	57.6953