Add the WebCoder-27B model card

README.md

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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}
+}
+```