commit 413b73d74bdaadb60e5a7ae209f14eed46aff9f6 Author: ModelHub XC Date: Wed May 6 14:18:48 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: SamuelBang/AesCoder-4B Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..1561479 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,53 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text + +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text + +vocab.json filter=lfs diff=lfs merge=lfs -text +model-00001-of-00002.safetensors filter=lfs diff=lfs merge=lfs -text +merges.txt filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text +model-00002-of-00002.safetensors filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..afdba05 --- /dev/null +++ b/README.md @@ -0,0 +1,332 @@ +--- +base_model: +- Qwen/Qwen3-4B-Instruct-2507 +pipeline_tag: text-generation +library_name: transformers +license: apache-2.0 +--- + +# [ICLR 2026] Code Aesthetics with Agentic Reward Feedback +
+ + Hugging Face +
+ Paper Link👁️ +
+
+

+1,2Bang Xiao#, + + 1,3Lingjie Jiang#, + + 1Shaohan Huang✉, + + 1Tengchao Lv, + + + 1Yupan Huang, + + + 1Xun Wu, + + + 1Lei Cui, + + + 1Furu Wei + +

+

+ 1Microsoft Research Asia    + 2Zhiyuan College, Shanghai Jiao Tong University    + 3Peking University
+ #Equal Contribution + ✉Corresponding author +

+
+ +For the codebase, refer to: https://github.com/bangx7/code_aesthetics + +## 🎉 News +- __[2026.01.12]__: Release the [AesCode](https://huggingface.co/datasets/SamuelBang/AesCode-358K) dataset. +- __[2025.10.29]__: Release the [AesCoder-4B](https://huggingface.co/SamuelBang/AesCoder-4B/) model. +- __[2025.10.27]__: Release the [Project Page](https://bangx7.github.io/code-aesthetics/) and the [Arxiv](https://arxiv.org/abs/2510.23272) version. + + +## 📷 Abstract +Large Language Models (LLMs) have become valuable assistants for developers in code-related tasks. While LLMs excel at traditional programming tasks such as code generation and bug fixing, they struggle with visually-oriented coding tasks, often producing suboptimal aesthetics. In this paper, we introduce a new pipeline to enhance the aesthetic quality of LLM-generated code. We first construct AesCode-358K, a large-scale instruction-tuning dataset focused on code aesthetics. Next, we propose agentic reward feedback, a multi-agent system that evaluates executability, static aesthetics, and interactive aesthetics. Building on this, we develop GRPO-AR, which integrates these signals into the GRPO algorithm for joint optimization of functionality and code aesthetics. Finally, we develop OpenDesign, a benchmark for assessing code aesthetics. Experimental results show that combining supervised fine-tuning on AesCode-358K with reinforcement learning using agentic reward feedback significantly improves performance on OpenDesign and also enhances results on existing benchmarks such as PandasPlotBench. Notably, our AesCoder-4B surpasses GPT-4o and GPT-4.1, and achieves performance comparable to large open-source models with 480B-685B parameters, underscoring the effectiveness of our approach. + +## To-do List +- [x] Release paper and project page +- [x] Release our AesCoder model +- [x] Release our AesCoder model +- [x] Release code + +**Note: This is the version of AesCoder-4B model for only webpage design.** + +## Quickstart +### VLLM deployment (Recommended) + +We recommend using `vllm>=0.8.5` for efficient inference and deployment. Here's how to get started: + +**Installation:** +```bash +pip install vllm>=0.8.5 +``` + +**API Server Deployment:** +To create an OpenAI-compatible API endpoint: +```bash +vllm serve SamuelBang/AesCoder-4B --max-model-len 262144 +``` + +**Using with OpenAI Client:** +```python +from openai import OpenAI + +# Initialize the client +client = OpenAI( + base_url="http://localhost:8000/v1", + api_key="None" +) + +# Generate completion +response = client.chat.completions.create( + model="SamuelBang/AesCoder-4B", + messages=[ + {"role": "user", "content": "Create a user-friendly website for a landing page dedicated to selling dog-related products."} + ], + temperature=0.8, + max_tokens=16384 +) + +# Get the generated content +content = response.choices[0].message.content +print("Generated content:", content) +``` + +**Basic vLLM Usage:** +```python +from vllm import LLM, SamplingParams + +model_name = "SamuelBang/AesCoder-4B" + +# Initialize the model +llm = LLM( + model=model_name, + max_model_len=262144, # Maximum context length + tensor_parallel_size=1, # Adjust based on your GPU setup +) + +# Define sampling parameters +sampling_params = SamplingParams( + temperature=0.8, + top_p=0.8, + top_k=20, + min_p=0, + max_tokens=16384 +) + +# Prepare the prompt +prompt = "Create a user-friendly website for a landing page dedicated to selling dog-related products. " +messages = [ + {"role": "user", "content": prompt} +] + +# Apply chat template (you'll need to get the tokenizer for this) +from transformers import AutoTokenizer +tokenizer = AutoTokenizer.from_pretrained(model_name) +text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, +) + +# Generate text +outputs = llm.generate([text], sampling_params) + +# Get the result +content = outputs[0].outputs[0].text +print("Generated content:", content) +``` + +**Note:** If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768` or adjusting the `tensor_parallel_size` based on your available GPU memory. + + + +### SGLang Deployment +You can use `sglang>=0.4.6.post1` to create an OpenAI-compatible API endpoint: + +```shell +python -m sglang.launch_server --model-path SamuelBang/AesCoder-4B --context-length 262144 +``` + + +**Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.** +### Use with origin `transformer` package (NOT recommended, very slow) + +The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. + +With `transformers<4.51.0`, you will encounter the following error: +``` +KeyError: 'qwen3' +``` + +The following contains a code snippet illustrating how to use the model generate content based on given inputs. +```python +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_name = "SamuelBang/AesCoder-4B" + +# load the tokenizer and the model +tokenizer = AutoTokenizer.from_pretrained(model_name) +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" +) + +# prepare the model input +prompt = "Create a user-friendly website for a landing page dedicated to selling dog-related products. " +messages = [ + {"role": "user", "content": prompt} +] +text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, +) +model_inputs = tokenizer([text], return_tensors="pt").to(model.device) + +# conduct text completion +generated_ids = model.generate( + **model_inputs, + max_new_tokens=16384, + temperature=0.8 +) +output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() + +content = tokenizer.decode(output_ids, skip_special_tokens=True) + +print("content:", content) +``` + +## Best Practices +### Sampling Parameters +To achieve optimal performance, we suggest using `Temperature=0.8`, `TopP=0.8`, `TopK=20`, and `MinP=0` for sampling. + +### System Prompt +To achieve better webpage generation results with our model, please use appropriate system prompts. We have categorized webpage generation into 5 main categories, and the recommended system prompts for each category are as follows (reference to: https://designarena.ai): + +Website: +```txt +You are an expert web developer and designer specializing in modern websites. Create a complete, working HTML page with embedded CSS and JavaScript if needed. Feel free to use lightweight libraries like Tailwind CSS to enhance the design as long as they can be rendered in an iframe. + Requirements: + 1. Create a fully functional, modern, and responsive website design + 2. Use only HTML, CSS, and JavaScript, but feel free to use libraries like Tailwind CSS to make the design better. Libraries such as ThreeJS, react three fiber, drei, @react-three/postprocessing, @react-three/cannon, d3, and recharts as additional libraries can be imported. + 3. Include all styles inline within