Instructions to use jkim96/Qwen3.5-9B-DASHQ-INT4-g32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jkim96/Qwen3.5-9B-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/Qwen3.5-9B-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/Qwen3.5-9B-DASHQ-INT4-g32", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jkim96/Qwen3.5-9B-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/Qwen3.5-9B-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/Qwen3.5-9B-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/Qwen3.5-9B-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/Qwen3.5-9B-DASHQ-INT4-g32

SGLang

How to use jkim96/Qwen3.5-9B-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/Qwen3.5-9B-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/Qwen3.5-9B-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/Qwen3.5-9B-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/Qwen3.5-9B-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/Qwen3.5-9B-DASHQ-INT4-g32 with Docker Model Runner:
```
docker model run hf.co/jkim96/Qwen3.5-9B-DASHQ-INT4-g32
```

jkim96 commited on May 28

Commit

fbcc3ec

verified ·

1 Parent(s): 6e6ce51

Update evaluation results

Browse files

Files changed (1) hide show

dashq_config.json +13 -11

dashq_config.json CHANGED Viewed

@@ -3002,15 +3002,17 @@
     "PPL": 8.678437232971191,
     "Params": "{'bits': 4, 'group_size': 32, 'scale_zero_dtype': 'float16', 'n_samples': 128, 'moe_hessian_scope': 'shared', 'use_error_compensation': True, 'use_optimal_shrinkage': True, 'use_weighted_quantization': True, 'symmetric': False, 'low_memory_optimization': False}",
     "QuantTime": 374.64265871047974,
-    "arc_challenge": 55.80204778156996,
-    "arc_easy": 74.74747474747475,
-    "commonsense_qa": 82.80098280098281,
-    "hellaswag": 77.61402111133239,
-    "lambada_openai": 69.92043469823403,
-    "openbookqa": 41.6,
-    "piqa": 79.86942328618063,
-    "truthfulqa_mc2": 53.0627493009839,
-    "winogrande": 72.21783741120757,
-    "zeroshot_avg": 67.51499679310734
   }
-}

     "PPL": 8.678437232971191,
     "Params": "{'bits': 4, 'group_size': 32, 'scale_zero_dtype': 'float16', 'n_samples': 128, 'moe_hessian_scope': 'shared', 'use_error_compensation': True, 'use_optimal_shrinkage': True, 'use_weighted_quantization': True, 'symmetric': False, 'low_memory_optimization': False}",
     "QuantTime": 374.64265871047974,
+    "arc_challenge": 55.71672354948806,
+    "arc_easy": 74.66329966329967,
+    "commonsense_qa": 82.96478296478297,
+    "gsm8k_cot": 89.23426838514025,
+    "hellaswag": 77.63393746265685,
+    "lambada_openai": 70.0950902386959,
+    "mmlu": 78.45748468879077,
+    "openbookqa": 42.0,
+    "piqa": 80.14145810663766,
+    "truthfulqa_mc2": 53.08014480902932,
+    "winogrande": 71.42857142857143,
+    "zeroshot_avg": 67.52488980257354
   }
+}