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 4B — AdamW step200

Full BF16 tool-using DesignCoder checkpoint, selected from xingxm/DesignCoder/designcoder_qwen3.5_4b_adamw_bs256_data41287_step200. This is an SFT initialization for DesignCoder RL. Its original weights, tokenizer, processor and chat template are preserved byte-for-byte, without quantization or LoRA merging.

The source run uses Qwen3.5-4B, AdamW (learning rate 2e-5), global batch 256 and 41,287 DesignCoder v2 trajectories. The export is step200, epoch 1.5066. The source model card records training on 8 nodes x 8 H20.

Tool protocol and inference

Use the system prompt and tools in DesignCoder runtime. Landing: design_search -> websearch -> HTML/CSS/JS. Dashboard: design_search -> HTML/CSS/JS. Calls are written as <tools>tool_name(...)</tools>, followed by tool observations. The final answer contains <code lang="html">, <code lang="css"> and <code lang="js"> blocks.

The original inference defaults are sampling with temperature 0.6, top_p 0.95, top_k 20, repetition_penalty 1.0 and thinking enabled. Tool turns allow 1,536 new tokens; the final turn requests 32,768 tokens, capped by remaining room in the 32,768-token context. Websearch requests four candidates per query. Use examples/designcoder/runtime/infer_designcoder.py or the aligned RL agent; a plain single-turn prompt does not reproduce the tool protocol.

Evaluation provenance

The retained 200-case evaluation scores 83.2948 under the explicitly named fixed-assets-layout-v1 aggregation. The corresponding 4B Muon step200 scores 76.2119 under the same aggregation. The original judge identity is not recorded. Older model-card numbers use other aggregation definitions and should not be mixed. No new benchmark or GPU evaluation is claimed by this upload.

Replacement and verification

This replaces the mistakenly selected Design.md-only AdamW step12588 checkpoint. That previous model remains in Git history and the archive tag archive-designmd-4b-step12588-20261109. The new root has one full model.safetensors; it has no adapter or old weight shards. upload_manifest.json records SHA256 values of every copied inference file. training_summary.json records the checkpoint identity and source evidence.

hf download chjdl1816/designcoder-mc --local-dir models/designcoder-4b-adamw-step200

Use a clean local directory when switching from an earlier export. The model architecture is Qwen3_5ForConditionalGeneration. DesignEvaluator is a separate frozen 9B model and is not included here.

Downloads last month: 46

Safetensors

Model size

5B params

Tensor type

BF16

Model tree for chjdl1816/designcoder-mc

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Finetuned

(1,314)

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