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

Publish selected local AdamW 4B SFT step12588, merged BF16 plus original LoRA

f1c28f5 verified 3 days ago

preview code

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---
license: apache-2.0
library_name: transformers
base_model: Qwen/Qwen3.5-4B
pipeline_tag: image-text-to-text
tags:
  - designcoder
  - qwen3_5
  - sft
  - lora
  - merged
  - web-design
---
# DesignCoder 4B — AdamW SFT, step 12588

This repository contains the **merged BF16 model** from a locally trained
Qwen3.5-4B LoRA SFT checkpoint, `qwen35-4b-design/lora-r64-sft/checkpoint-12588`.
The root directory is a complete Hugging Face model export; loading it does not
require PEFT or a separate base-model download.

This is the AdamW checkpoint selected for DesignCoder RL initialization. It is
an SFT model, not an RL-trained checkpoint. It replaces the previously uploaded
Muon step1900 model on the main branch; that version remains in repository history.

## Source and training

| Item | Value |
| --- | --- |
| Base | [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B/tree/851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a) |
| Base revision | `851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a` |
| Optimizer | `adamw_torch` |
| Checkpoint | step 12588, 2 epochs |
| Dataset identifier | `design_sft_sharegpt_with_think` |
| LoRA | rank 64, alpha 128, dropout 0.05 |
| Learning rate | `1e-4`, cosine schedule, warmup ratio 0.03 |
| Training batch | 1 per device, 8 accumulation steps, effective batch 8 |
| Training cutoff | 16384 tokens |
| Export | FP32 LoRA merge with `safe_merge=True`, then BF16 safetensors |

The tokenizer, processor configuration and chat template are copied from the
selected training checkpoint. The vocabulary matches the base model. Standard
Transformers loading omits the base checkpoint's auxiliary MTP tensors, which
are not used by `Qwen3_5ForConditionalGeneration`.

The original adapter weights are also included under [`adapter/`](adapter/).
Their SHA256 is
`7d8305739df470c3d7906241a14ae37b342e1ba61f34b3d0341e69b97f85f12b`.
The adapter config uses the public base-model ID and pinned revision instead of
the original local filesystem path. Do not apply this adapter to the already
merged model at the repository root.

## Load the merged model

The export was checked with Transformers 5.8.0, PEFT 0.18.1 and PyTorch 2.6.0.

```python
from transformers import AutoTokenizer, Qwen3_5ForConditionalGeneration
model_id = "chjdl1816/designcoder-mc"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id, dtype="auto", device_map="auto"
)
messages = [{"role": "user", "content": "Build a minimal portfolio homepage."}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=True
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(output[0, inputs.input_ids.shape[1]:], skip_special_tokens=True))
```

For reproducible training, pin the repository commit when downloading.

## Validation and artifacts

- All source adapter tensors were compared against their loaded values.
- FP32 inference before and after the LoRA merge was compared on a short text prompt.
- The BF16 export was reloaded and its logits were checked for exact equality
  with the in-memory BF16 model on that prompt.
- Tokenizer, processor and model configuration loading were checked.
- [`merge_validation.json`](merge_validation.json) contains the numerical results;
  [`training_summary.json`](training_summary.json) records training metadata;
  [`upload_manifest.json`](upload_manifest.json) records source and artifact hashes.

These checks validate export integrity. No new benchmark, GPU inference or
vLLM end-to-end RL evaluation was performed for this upload.

## License

Apache-2.0, following the Qwen3.5-4B base model. See [LICENSE](LICENSE).

	---
	license: apache-2.0
	library_name: transformers
	base_model: Qwen/Qwen3.5-4B
	pipeline_tag: image-text-to-text
	tags:
	- designcoder
	- qwen3_5
	- sft
	- lora
	- merged
	- web-design
	---

	# DesignCoder 4B — AdamW SFT, step 12588

	This repository contains the merged BF16 model from a locally trained
	Qwen3.5-4B LoRA SFT checkpoint, `qwen35-4b-design/lora-r64-sft/checkpoint-12588`.
	The root directory is a complete Hugging Face model export; loading it does not
	require PEFT or a separate base-model download.

	This is the AdamW checkpoint selected for DesignCoder RL initialization. It is
	an SFT model, not an RL-trained checkpoint. It replaces the previously uploaded
	Muon step1900 model on the main branch; that version remains in repository history.

	## Source and training

	\| Item \| Value \|
	\| --- \| --- \|
	\| Base \| [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B/tree/851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a) \|
	\| Base revision \| `851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a` \|
	\| Optimizer \| `adamw_torch` \|
	\| Checkpoint \| step 12588, 2 epochs \|
	\| Dataset identifier \| `design_sft_sharegpt_with_think` \|
	\| LoRA \| rank 64, alpha 128, dropout 0.05 \|
	\| Learning rate \| `1e-4`, cosine schedule, warmup ratio 0.03 \|
	\| Training batch \| 1 per device, 8 accumulation steps, effective batch 8 \|
	\| Training cutoff \| 16384 tokens \|
	\| Export \| FP32 LoRA merge with `safe_merge=True`, then BF16 safetensors \|

	The tokenizer, processor configuration and chat template are copied from the
	selected training checkpoint. The vocabulary matches the base model. Standard
	Transformers loading omits the base checkpoint's auxiliary MTP tensors, which
	are not used by `Qwen3_5ForConditionalGeneration`.

	The original adapter weights are also included under [`adapter/`](adapter/).
	Their SHA256 is
	`7d8305739df470c3d7906241a14ae37b342e1ba61f34b3d0341e69b97f85f12b`.
	The adapter config uses the public base-model ID and pinned revision instead of
	the original local filesystem path. Do not apply this adapter to the already
	merged model at the repository root.

	## Load the merged model

	The export was checked with Transformers 5.8.0, PEFT 0.18.1 and PyTorch 2.6.0.

	```python
	from transformers import AutoTokenizer, Qwen3_5ForConditionalGeneration

	model_id = "chjdl1816/designcoder-mc"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = Qwen3_5ForConditionalGeneration.from_pretrained(
	model_id, dtype="auto", device_map="auto"
	)
	messages = [{"role": "user", "content": "Build a minimal portfolio homepage."}]
	text = tokenizer.apply_chat_template(
	messages, tokenize=False, add_generation_prompt=True, enable_thinking=True
	)
	inputs = tokenizer(text, return_tensors="pt").to(model.device)
	output = model.generate(**inputs, max_new_tokens=1024)
	print(tokenizer.decode(output[0, inputs.input_ids.shape[1]:], skip_special_tokens=True))
	```

	For reproducible training, pin the repository commit when downloading.

	## Validation and artifacts

	- All source adapter tensors were compared against their loaded values.
	- FP32 inference before and after the LoRA merge was compared on a short text prompt.
	- The BF16 export was reloaded and its logits were checked for exact equality
	with the in-memory BF16 model on that prompt.
	- Tokenizer, processor and model configuration loading were checked.
	- [`merge_validation.json`](merge_validation.json) contains the numerical results;
	[`training_summary.json`](training_summary.json) records training metadata;
	[`upload_manifest.json`](upload_manifest.json) records source and artifact hashes.

	These checks validate export integrity. No new benchmark, GPU inference or
	vLLM end-to-end RL evaluation was performed for this upload.

	## License

	Apache-2.0, following the Qwen3.5-4B base model. See [LICENSE](LICENSE).