Instructions to use jkim96/Qwen3.5-35B-A3B-DASHQ-INT4-g32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jkim96/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-DASHQ-INT4-g32")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("jkim96/Qwen3.5-35B-A3B-DASHQ-INT4-g32")
model = AutoModelForMultimodalLM.from_pretrained("jkim96/Qwen3.5-35B-A3B-DASHQ-INT4-g32")
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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-DASHQ-INT4-g32

SGLang

How to use jkim96/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-DASHQ-INT4-g32 with Docker Model Runner:
```
docker model run hf.co/jkim96/Qwen3.5-35B-A3B-DASHQ-INT4-g32
```

Qwen3.5-35B-A3B-DASHQ-INT4-g32

This repository contains a DASH-Q packed quantized checkpoint for Qwen/Qwen3.5-35B-A3B.

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/Qwen3.5-35B-A3B-DASHQ-INT4-g32",
    device_map="auto",
)

Quantization

Field	Value
Base model	`Qwen/Qwen3.5-35B-A3B`
Bits	`4`
Group size	`32`
Scale/zero dtype	`float16`
Calibration dataset	`wikitext2`
Calibration samples	`128`
Sequence length	`2048`
Original size	`71.9039 GB`
Quantized size	`23.9941 GB`

Evaluation

Metric	Value
`wikitext2_ppl`	6.6539
`zero-shot accuracy avg`	70.7482
`arc_challenge`	61.4334
`arc_easy`	80.0505
`commonsense_qa`	84.7666
`gsm8k_cot`	87.8696
`hellaswag`	82.1350
`lambada_openai`	72.7343
`mmlu`	83.1434
`openbookqa`	43.8000
`piqa`	83.0250
`truthfulqa_mc2`	53.8083
`winogrande`	74.9803