Text Generation
Transformers
Safetensors
designcoder
ui-generation
front-end
html
css
javascript
code-generation
full-sft
Instructions to use xingxm/DesignCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xingxm/DesignCoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="xingxm/DesignCoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xingxm/DesignCoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use xingxm/DesignCoder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "xingxm/DesignCoder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xingxm/DesignCoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/xingxm/DesignCoder
- SGLang
How to use xingxm/DesignCoder 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 "xingxm/DesignCoder" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xingxm/DesignCoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "xingxm/DesignCoder" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xingxm/DesignCoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use xingxm/DesignCoder with Docker Model Runner:
docker model run hf.co/xingxm/DesignCoder
Add eval artifacts: summary, per-case scores, significance tests, rubric stats, HTML report
Browse files
eval/benchmark_summary.csv
ADDED
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model,n,overall,overall_A_landing,overall_A_dashboard,overall_B_landing,overall_B_dashboard,overall_landing,overall_dashboard,track_A,track_B,dim_alignment,dim_layout,dim_typography,dim_components,dim_assets,dim_aesthetics,prompt_fit,frozen_pass_rate,render_fail,std
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27B_AdamW_step400,200,87.89,87.84,84.56,90.92,89.48,88.55,86.67,86.90,90.20,89.77,91.81,91.12,89.58,67.12,96.29,84.10,88.60,0,9.54
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| 3 |
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9B_AdamW_step200,200,84.40,85.92,79.56,87.61,82.57,86.31,80.85,84.10,85.09,87.01,87.53,89.88,85.17,62.84,95.27,78.35,85.37,1,10.45
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4B_AdamW_step200,200,82.12,84.70,76.61,86.30,76.69,85.07,76.65,82.39,81.50,83.77,87.77,86.92,81.81,62.33,93.35,75.25,83.24,1,12.49
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| 5 |
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4B_Muon_step200,200,77.62,81.09,69.01,83.50,71.65,81.65,70.14,77.64,77.58,80.84,83.85,85.67,77.46,55.48,90.15,64.60,79.43,3,13.76
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