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 / README.md

chjdl1816

Replace Design.md-only checkpoint with tool-using DesignCoder 4B AdamW step200

f602de6 verified about 8 hours ago

preview code

Raw History Blame Contribute Delete

2.8 kB

---
license: apache-2.0
base_model: Qwen/Qwen3.5-4B
library_name: transformers
pipeline_tag: image-text-to-text
tags: [designcoder, tool-use, web-design, sft]
---
# 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](https://github.com/ZitengXue/DesignCoder/tree/main/examples/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.

```bash
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.

	---
	license: apache-2.0
	base_model: Qwen/Qwen3.5-4B
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags: [designcoder, tool-use, web-design, sft]
	---

	# 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](https://github.com/ZitengXue/DesignCoder/tree/main/examples/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.

	```bash
	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.