Instructions to use chjdl1816/designcoder-mc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chjdl1816/designcoder-mc with Transformers:

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

pipe = pipeline("image-text-to-text", model="chjdl1816/designcoder-mc")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("chjdl1816/designcoder-mc")
model = AutoModelForMultimodalLM.from_pretrained("chjdl1816/designcoder-mc", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chjdl1816/designcoder-mc with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chjdl1816/designcoder-mc"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chjdl1816/designcoder-mc",
		"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/chjdl1816/designcoder-mc

SGLang

How to use chjdl1816/designcoder-mc 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 "chjdl1816/designcoder-mc" \
    --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": "chjdl1816/designcoder-mc",
		"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 "chjdl1816/designcoder-mc" \
        --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": "chjdl1816/designcoder-mc",
		"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 chjdl1816/designcoder-mc with Docker Model Runner:
```
docker model run hf.co/chjdl1816/designcoder-mc
```

designcoder-mc

File size: 3,582 Bytes

{
  "repository": "chjdl1816/designcoder-mc",
  "source_checkpoint": "qwen35-4b-design/lora-r64-sft/checkpoint-12588",
  "source_adapter_sha256": "7d8305739df470c3d7906241a14ae37b342e1ba61f34b3d0341e69b97f85f12b",
  "base_model": "Qwen/Qwen3.5-4B",
  "base_revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a",
  "source_base_files": {
    "model.safetensors-00001-of-00002.safetensors": {
      "bytes": 5329398688,
      "sha256": "26a93f066e1916adb13453dae5a0c707c0fbc71299ed98779571a907b8e74c61"
    },
    "model.safetensors-00002-of-00002.safetensors": {
      "bytes": 3990429408,
      "sha256": "cb544bd9bfae93dc59b0f22b292f5933573854a7f9b97835c67060d7d910e188"
    },
    "config.json": {
      "bytes": 3161,
      "sha256": "ddc63e1c717afa86c865bb5e01313d89d72bb53b97ad4a8a03ba8510c0621670"
    }
  },
  "files": {
    "LICENSE": {
      "bytes": 11544,
      "sha256": "bbedc3fda3305820b977265f01b8619d87570a6739de3a5582c3464840f1e57a"
    },
    "adapter/adapter_config.json": {
      "bytes": 1172,
      "sha256": "7bd5a23542725c33d35bef0a9f6e75508265641ed29c547e1187951b89ceea12"
    },
    "adapter/adapter_model.safetensors": {
      "bytes": 519513832,
      "sha256": "7d8305739df470c3d7906241a14ae37b342e1ba61f34b3d0341e69b97f85f12b"
    },
    "adapter/chat_template.jinja": {
      "bytes": 7756,
      "sha256": "a4aee8afcf2e0711942cf848899be66016f8d14a889ff9ede07bca099c28f715"
    },
    "adapter/processor_config.json": {
      "bytes": 1191,
      "sha256": "d89ef49ce9cd37fbf510158e13c1ef063d9286411c1ec9049932dbe0487143b1"
    },
    "adapter/tokenizer.json": {
      "bytes": 19989325,
      "sha256": "06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523"
    },
    "adapter/tokenizer_config.json": {
      "bytes": 1192,
      "sha256": "4a5bac6704ca726f9a48fe654a01eb5ea4d01d9d498aaf23035c74fa2a4f240a"
    },
    "chat_template.jinja": {
      "bytes": 7756,
      "sha256": "a4aee8afcf2e0711942cf848899be66016f8d14a889ff9ede07bca099c28f715"
    },
    "config.json": {
      "bytes": 2827,
      "sha256": "e293f558a7d5436955a79c7152685ef8b02d8bec8e49950c234d31c09109ebc7"
    },
    "generation_config.json": {
      "bytes": 163,
      "sha256": "d22c54ca271816e74169ce8bd66d70b0a97358917c6a22b00547d196e850b9eb"
    },
    "merge_validation.json": {
      "bytes": 1492,
      "sha256": "73e05cf2b79cfed0a5be5139a53fb5d21e072d796b99fb555439b651f313f392"
    },
    "model-00001-of-00002.safetensors": {
      "bytes": 4972947968,
      "sha256": "e3b3dd2707eed9c76ac07079b6e64796691611d24fa8bc640b6c91b8842f6a0f"
    },
    "model-00002-of-00002.safetensors": {
      "bytes": 4105672464,
      "sha256": "53d6f86f7467068482f57c1a2dce90da326aeb549b3d0a5de364a3438921fa34"
    },
    "model.safetensors.index.json": {
      "bytes": 66236,
      "sha256": "df59e0e8f68ce1841a11ec0ba05e468a1413a7c5df5d1fa1e8b29a911a1b29d1"
    },
    "processor_config.json": {
      "bytes": 1191,
      "sha256": "d89ef49ce9cd37fbf510158e13c1ef063d9286411c1ec9049932dbe0487143b1"
    },
    "tokenizer.json": {
      "bytes": 19989325,
      "sha256": "06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523"
    },
    "tokenizer_config.json": {
      "bytes": 1192,
      "sha256": "4a5bac6704ca726f9a48fe654a01eb5ea4d01d9d498aaf23035c74fa2a4f240a"
    },
    "training_summary.json": {
      "bytes": 905,
      "sha256": "e4d3e3f9a66d1b748e481f029e347e2733c7bb51a1846f2ab2a2bd739422e245"
    },
    "README.md": {
      "bytes": 3778,
      "sha256": "3568d88e36cfc8e150337119c203d4a33c6776de9dd083027c55981b71cadd99"
    }
  }
}