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")# 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
File size: 2,795 Bytes
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"summary": {
"api_azure_openai_gpt-5.2": {
"label": "GPT-5.2",
"n_cases": 200,
"n_generated": 200,
"overall": 89.9,
"overall_no_vsd": 88.38,
"prompt_fit": 88.33,
"frozen": 86.37,
"overall_landing": 92.09,
"n_landing": 130,
"overall_dashboard": 85.83,
"n_dashboard": 70,
"overall_track_A": 89.64,
"overall_track_B": 90.51
},
"claude-opus-4-6-v1": {
"label": "Claude Opus 4.6",
"n_cases": 200,
"n_generated": 200,
"overall": 86.91,
"overall_no_vsd": 84.9,
"prompt_fit": 73.38,
"frozen": 73.01,
"overall_landing": 82.86,
"n_landing": 130,
"overall_dashboard": 94.42,
"n_dashboard": 70,
"overall_track_A": 85.74,
"overall_track_B": 89.64
},
"claude-sonnet-4.6": {
"label": "Claude Sonnet 4.6",
"n_cases": 200,
"n_generated": 200,
"overall": 86.12,
"overall_no_vsd": 83.98,
"prompt_fit": 68.9,
"frozen": 67.66,
"overall_landing": 82.62,
"n_landing": 130,
"overall_dashboard": 92.61,
"n_dashboard": 70,
"overall_track_A": 85.09,
"overall_track_B": 88.51
},
"deepseek-v4-flash": {
"label": "DeepSeek-V4 Flash",
"n_cases": 200,
"n_generated": 200,
"overall": 89.17,
"overall_no_vsd": 87.4,
"prompt_fit": 74.17,
"frozen": 71.77,
"overall_landing": 86.52,
"n_landing": 130,
"overall_dashboard": 94.1,
"n_dashboard": 70,
"overall_track_A": 88.82,
"overall_track_B": 89.99
},
"deepseek-v4-pro": {
"label": "DeepSeek-V4 Pro",
"n_cases": 200,
"n_generated": 200,
"overall": 88.89,
"overall_no_vsd": 87.19,
"prompt_fit": 78.03,
"frozen": 78.52,
"overall_landing": 87.45,
"n_landing": 130,
"overall_dashboard": 91.56,
"n_dashboard": 70,
"overall_track_A": 88.14,
"overall_track_B": 90.64
},
"glm-5.1": {
"label": "GLM-5.1",
"n_cases": 200,
"n_generated": 200,
"overall": 79.66,
"overall_no_vsd": 76.77,
"prompt_fit": 59.69,
"frozen": 60.7,
"overall_landing": 75.23,
"n_landing": 130,
"overall_dashboard": 87.91,
"n_dashboard": 70,
"overall_track_A": 76.77,
"overall_track_B": 86.41
},
"kimi-k2.6": {
"label": "Kimi-K2.6",
"n_cases": 200,
"n_generated": 200,
"overall": 79.58,
"overall_no_vsd": 76.55,
"prompt_fit": 57.61,
"frozen": 56.94,
"overall_landing": 74.99,
"n_landing": 130,
"overall_dashboard": 88.1,
"n_dashboard": 70,
"overall_track_A": 78.18,
"overall_track_B": 82.83
}
},
"n_rows": 1400
} |