Instructions to use jkim96/gemma-4-26B-A4B-it-DASHQ-INT4-g32 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-INT4-g32 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-INT4-g32", trust_remote_code=True)
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, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("jkim96/gemma-4-26B-A4B-it-DASHQ-INT4-g32", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jkim96/gemma-4-26B-A4B-it-DASHQ-INT4-g32", trust_remote_code=True, device_map="auto")
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 Settings

vLLM

How to use jkim96/gemma-4-26B-A4B-it-DASHQ-INT4-g32 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-INT4-g32"
# 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-INT4-g32",
		"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-INT4-g32

SGLang

How to use jkim96/gemma-4-26B-A4B-it-DASHQ-INT4-g32 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-INT4-g32" \
    --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-INT4-g32",
		"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-INT4-g32" \
        --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-INT4-g32",
		"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-INT4-g32 with Docker Model Runner:
```
docker model run hf.co/jkim96/gemma-4-26B-A4B-it-DASHQ-INT4-g32
```

gemma-4-26B-A4B-it-DASHQ-INT4-g32 / README.md

jkim96

Add DASH-Q remote-code inference (Triton decode kernel)

60a7613 verified 29 days ago

preview code

Raw

History Blame Contribute Delete

2.1 kB

	---
	license: apache-2.0
	pipeline_tag: image-text-to-text
	base_model: google/gemma-4-26B-A4B-it
	base_model_relation: quantized
	library_name: transformers
	tags:
	- dashq
	- quantized
	- post-training-quantization
	- int4
	---
	![DASH-Q](https://raw.githubusercontent.com/JaeminK/dashq/main/assets/dashq_banner.png)

	# gemma-4-26B-A4B-it-DASHQ-INT4-g32

	> DASH-Q — Diagonal-Aware Shrinkage for Robust PTQ.
	> `INT4` · group size 32 · 17.9357 GB (from 51.6120 GB — 2.9x smaller)

	## Usage

	```python
	from transformers import AutoModelForImageTextToText, AutoTokenizer

	model = AutoModelForImageTextToText.from_pretrained(
	"jkim96/gemma-4-26B-A4B-it-DASHQ-INT4-g32", trust_remote_code=True, device_map="cuda", dtype="auto"
	)
	tokenizer = AutoTokenizer.from_pretrained("jkim96/gemma-4-26B-A4B-it-DASHQ-INT4-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

	\| Package \| Minimum \| Verified with \|
	\| --- \| --- \| --- \|
	\| `torch` \| 2.4 \| 2.12.1+cu130 \|
	\| `transformers` \| 5.8 \| 5.9.0 \|
	\| `triton` \| 3.0 (Linux; bundled with CUDA builds of PyTorch) \| 3.7.1 \|
	\| `huggingface_hub` \| 1.5 (pulled in by transformers) \| 1.15.0 \|

	## Quantization

	\| Field \| Value \|
	\| --- \| --- \|
	\| Base model \| `google/gemma-4-26B-A4B-it` \|
	\| Precision \| INT4, group size 32 \|
	\| Scale / zero dtype \| float16 \|
	\| Calibration \| wikitext2, 128 samples x 2048 \|
	\| Size \| 17.9357 GB · original 51.6120 GB · 2.9x compression \|

	## Benchmarks

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