Instructions to use Roy-DOU/WebCoder-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Roy-DOU/WebCoder-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Roy-DOU/WebCoder-27B")
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("Roy-DOU/WebCoder-27B")
model = AutoModelForMultimodalLM.from_pretrained("Roy-DOU/WebCoder-27B", 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 Roy-DOU/WebCoder-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Roy-DOU/WebCoder-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Roy-DOU/WebCoder-27B",
		"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/Roy-DOU/WebCoder-27B

SGLang

How to use Roy-DOU/WebCoder-27B 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 "Roy-DOU/WebCoder-27B" \
    --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": "Roy-DOU/WebCoder-27B",
		"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 "Roy-DOU/WebCoder-27B" \
        --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": "Roy-DOU/WebCoder-27B",
		"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 Roy-DOU/WebCoder-27B with Docker Model Runner:
```
docker model run hf.co/Roy-DOU/WebCoder-27B
```

WebCoder-27B / README.md

Roy-DOU

Add the WebCoder-27B model card

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---
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
base_model: Qwen/Qwen3.8-27B
base_model_relation: finetune
datasets:
  - Roy-DOU/Webdev-Bench
tags:
  - agent
  - code
  - frontend
  - web-development
  - reinforcement-learning
---
# WebCoder-27B

[Project page](https://douzj-llm.github.io/WebCoder/) ·
[Code](https://github.com/douzj-llm/WebCoder) ·
[Webdev-Bench](https://huggingface.co/datasets/Roy-DOU/Webdev-Bench) ·
[Paper](https://arxiv.org/abs/2610.23104)

WebCoder-27B is a general frontend development agent, trained from
[Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) with artifact-grounded agentic
reinforcement learning. It is the model released with the paper

**WebCoder: Scaling General Frontend Development with Artifact-Grounded Agentic
Reinforcement Learning**
Zijun Dou\*, Zhen Yang\*, Xijun Liu, Dongzhuoran Zhou, Aohan Zeng, Jie Tang
*\*Core contributors.*

## Overview

Real-world frontend development requires an agent to compose reproduction, generation,
modification and repair over an evolving application state. WebCoder assesses all of these
activities through their effects on a shared executable artifact and on its satisfaction of
the task requirements.

- **Webdev-World** — an executable environment that integrates code editing, application
  execution, browser inspection and on-demand skills, preserving project state across
  interactions.
- **Artifact-grounded reward and credit assignment** — an independent verifier evaluates the
  final application against the task requirements to give a terminal reward; changes in
  intermediate artifact progress redistribute the resulting rollout advantage across
  development segments.
- **Long-horizon training** — context compaction and message-tree sample construction support
  training on trajectories beyond a single context window, without repeated loss computation
  on shared generated history.

## Results

| Model | Vision2Web Avg | Webdev-Bench RS | Webdev-Bench Avg | Webdev-Bench DSR |
|---|---:|---:|---:|---:|
| Claude-Opus-4.8 | 62.5 | 65.1 | 73.7 | 100.0% |
| GPT-5.4 | 57.9 | 59.6 | 70.8 | 100.0% |
| Qwen3.8-27B (base) | 41.1 | 64.8 | 72.8 | 95.0% |
| **WebCoder-27B** | **62.4** | **72.8** | **79.8** | **100.0%** |

RS is requirement satisfaction and DSR the delivery success rate. The full table, with every
baseline and every sub-metric, is in the paper and on the
[project page](https://douzj-llm.github.io/WebCoder/#results).

## Usage

WebCoder-27B keeps the architecture (`Qwen3_5ForConditionalGeneration`), tokenizer, chat
template and processor configuration of its base model, so it is served the same way as
Qwen3.8-27B — for example with vLLM or SGLang. See the
[Qwen3.8-27B model card](https://huggingface.co/Qwen/Qwen3.8-27B) for serving commands and
API examples.

```bash
vllm serve Roy-DOU/WebCoder-27B --tensor-parallel-size 4
```

`generation_config.json` sets `temperature=1.0`, `top_p=0.95`, `top_k=20`.

The model was trained as an agent: it acts through tools in a development environment over
many turns, and is at its best in that setting rather than in single-turn chat.

## License

Released under the Apache License 2.0, the license of the base model. See `LICENSE`.

## Citation

```bibtex
@article{dou2026webcoder,
  title   = {WebCoder: Scaling General Frontend Development with Artifact-Grounded Agentic Reinforcement Learning},
  author  = {Dou, Zijun and Yang, Zhen and Liu, Xijun and Zhou, Dongzhuoran and Zeng, Aohan and Tang, Jie},
  journal = {arXiv preprint arXiv:2610.23104},
  year    = {2026}
}
```

	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: image-text-to-text
	base_model: Qwen/Qwen3.8-27B
	base_model_relation: finetune
	datasets:
	- Roy-DOU/Webdev-Bench
	tags:
	- agent
	- code
	- frontend
	- web-development
	- reinforcement-learning
	---

	# WebCoder-27B

	[Project page](https://douzj-llm.github.io/WebCoder/) ·
	[Code](https://github.com/douzj-llm/WebCoder) ·
	[Webdev-Bench](https://huggingface.co/datasets/Roy-DOU/Webdev-Bench) ·
	[Paper](https://arxiv.org/abs/2610.23104)

	WebCoder-27B is a general frontend development agent, trained from
	[Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) with artifact-grounded agentic
	reinforcement learning. It is the model released with the paper

	**WebCoder: Scaling General Frontend Development with Artifact-Grounded Agentic
	Reinforcement Learning**
	Zijun Dou\, Zhen Yang\, Xijun Liu, Dongzhuoran Zhou, Aohan Zeng, Jie Tang
	\Core contributors.*

	## Overview

	Real-world frontend development requires an agent to compose reproduction, generation,
	modification and repair over an evolving application state. WebCoder assesses all of these
	activities through their effects on a shared executable artifact and on its satisfaction of
	the task requirements.

	- Webdev-World — an executable environment that integrates code editing, application
	execution, browser inspection and on-demand skills, preserving project state across
	interactions.
	- Artifact-grounded reward and credit assignment — an independent verifier evaluates the
	final application against the task requirements to give a terminal reward; changes in
	intermediate artifact progress redistribute the resulting rollout advantage across
	development segments.
	- Long-horizon training — context compaction and message-tree sample construction support
	training on trajectories beyond a single context window, without repeated loss computation
	on shared generated history.

	## Results

	\| Model \| Vision2Web Avg \| Webdev-Bench RS \| Webdev-Bench Avg \| Webdev-Bench DSR \|
	\|---\|---:\|---:\|---:\|---:\|
	\| Claude-Opus-4.8 \| 62.5 \| 65.1 \| 73.7 \| 100.0% \|
	\| GPT-5.4 \| 57.9 \| 59.6 \| 70.8 \| 100.0% \|
	\| Qwen3.8-27B (base) \| 41.1 \| 64.8 \| 72.8 \| 95.0% \|
	\| WebCoder-27B \| 62.4 \| 72.8 \| 79.8 \| 100.0% \|

	RS is requirement satisfaction and DSR the delivery success rate. The full table, with every
	baseline and every sub-metric, is in the paper and on the
	[project page](https://douzj-llm.github.io/WebCoder/#results).

	## Usage

	WebCoder-27B keeps the architecture (`Qwen3_5ForConditionalGeneration`), tokenizer, chat
	template and processor configuration of its base model, so it is served the same way as
	Qwen3.8-27B — for example with vLLM or SGLang. See the
	[Qwen3.8-27B model card](https://huggingface.co/Qwen/Qwen3.8-27B) for serving commands and
	API examples.

	```bash
	vllm serve Roy-DOU/WebCoder-27B --tensor-parallel-size 4
	```

	`generation_config.json` sets `temperature=1.0`, `top_p=0.95`, `top_k=20`.

	The model was trained as an agent: it acts through tools in a development environment over
	many turns, and is at its best in that setting rather than in single-turn chat.

	## License

	Released under the Apache License 2.0, the license of the base model. See `LICENSE`.

	## Citation

	```bibtex
	@article{dou2026webcoder,
	title = {WebCoder: Scaling General Frontend Development with Artifact-Grounded Agentic Reinforcement Learning},
	author = {Dou, Zijun and Yang, Zhen and Liu, Xijun and Zhou, Dongzhuoran and Zeng, Aohan and Tang, Jie},
	journal = {arXiv preprint arXiv:2610.23104},
	year = {2026}
	}
	```