Instructions to use jkim96/Qwen3.5-27B-DASHQ-INT4-g128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jkim96/Qwen3.5-27B-DASHQ-INT4-g128 with Transformers:

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

pipe = pipeline("image-text-to-text", model="jkim96/Qwen3.5-27B-DASHQ-INT4-g128", 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/Qwen3.5-27B-DASHQ-INT4-g128", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jkim96/Qwen3.5-27B-DASHQ-INT4-g128", 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/Qwen3.5-27B-DASHQ-INT4-g128 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jkim96/Qwen3.5-27B-DASHQ-INT4-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/Qwen3.5-27B-DASHQ-INT4-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/Qwen3.5-27B-DASHQ-INT4-g128

SGLang

How to use jkim96/Qwen3.5-27B-DASHQ-INT4-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/Qwen3.5-27B-DASHQ-INT4-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/Qwen3.5-27B-DASHQ-INT4-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/Qwen3.5-27B-DASHQ-INT4-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/Qwen3.5-27B-DASHQ-INT4-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/Qwen3.5-27B-DASHQ-INT4-g128 with Docker Model Runner:
```
docker model run hf.co/jkim96/Qwen3.5-27B-DASHQ-INT4-g128
```

jkim96 commited on May 28

Commit

735d43f

verified ·

1 Parent(s): 0616061

Update evaluation results

Browse files

Files changed (1) hide show

README.md +10 -8

README.md CHANGED Viewed

@@ -50,14 +50,16 @@ model, tokenizer = load_quantized(
 | Metric | Value |
 | --- | ---: |
 | `wikitext2_ppl` | 6.9914 |
-| `zero-shot accuracy avg` | 72.3486 |
-| `arc_challenge` | 61.3481 |
 | `arc_easy` | 80.7660 |
-| `commonsense_qa` | 87.7150 |
-| `hellaswag` | 82.8719 |
-| `lambada_openai` | 75.3736 |
 | `openbookqa` | 44.0000 |
-| `piqa` | 82.2089 |
-| `truthfulqa_mc2` | 57.8483 |
-| `winogrande` | 79.0055 |

 | Metric | Value |
 | --- | ---: |
 | `wikitext2_ppl` | 6.9914 |
+| `zero-shot accuracy avg` | 72.4096 |
+| `arc_challenge` | 61.9454 |
 | `arc_easy` | 80.7660 |
+| `commonsense_qa` | 87.5512 |
+| `gsm8k_cot` | 91.5845 |
+| `hellaswag` | 82.8520 |
+| `lambada_openai` | 75.2377 |
+| `mmlu` | 86.8822 |
 | `openbookqa` | 44.0000 |
+| `piqa` | 82.2633 |
+| `truthfulqa_mc2` | 57.8280 |
+| `winogrande` | 79.2423 |