Instructions to use Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16")
model = AutoModelForMultimodalLM.from_pretrained("Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16", 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 Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16",
		"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/Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16

SGLang

How to use Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16 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 "Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16" \
    --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": "Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16",
		"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 "Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16" \
        --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": "Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16",
		"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 Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16 with Docker Model Runner:
```
docker model run hf.co/Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16
```

Widget2Code Qwen3.5-27B SFT (merged BF16)

This repository contains full BF16 weights for a Widget2Code SFT model based on Qwen/Qwen3.5-27B. A rank-32 LoRA adapter (lora_alpha=64) was merged into the base model with PEFT merge_and_unload(safe_merge=True).

Artifact details

Base model: Qwen/Qwen3.5-27B
Format: Hugging Face Transformers safetensors
Saved parameters: 27,356,728,560
Weight dtype: BF16
Adapter training dataset name: widget2code_sft

merge_provenance.json records the base revision, adapter configuration, adapter hashes, merge environment, and audited tensor inventory. The saved weights contain no remaining LoRA tensors.

Usage

from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    dtype="auto",
    device_map="auto",
)

Limitations

No standalone benchmark results are included in this model card. Users should evaluate the model on their target screenshot-to-code distribution before deployment.

Downloads last month: 9

Safetensors

Model size

27B params

Tensor type

BF16

Model tree for Djanghao/widget2code-qwen3.5-27b-size-tools-merged-bf16

Base model

Qwen/Qwen3.5-27B

Finetuned

(303)

this model