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/significance_tests.json +62 -0
eval/significance_tests.json
ADDED
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{
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"27B_AdamW_step400_vs_9B_AdamW_step200": {
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"mean_diff": 3.493,
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"bootstrap_ci95": [
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1.842,
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5.136
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],
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"wilcoxon_W": 5676.0,
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"wilcoxon_p": 7.6426218939254e-06,
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"significant_at_0.05": true
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},
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"27B_AdamW_step400_vs_4B_AdamW_step200": {
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"mean_diff": 5.771,
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"bootstrap_ci95": [
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3.993,
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7.612
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],
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"wilcoxon_W": 4807.5,
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"wilcoxon_p": 7.459408306871403e-09,
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"significant_at_0.05": true
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},
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"27B_AdamW_step400_vs_4B_Muon_step200": {
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"mean_diff": 10.271,
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"bootstrap_ci95": [
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8.413,
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12.196
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],
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"wilcoxon_W": 2153.5,
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"wilcoxon_p": 4.759420773419259e-20,
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"significant_at_0.05": true
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},
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"9B_AdamW_step200_vs_4B_AdamW_step200": {
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"mean_diff": 2.278,
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"bootstrap_ci95": [
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0.53,
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4.007
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],
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"wilcoxon_W": 7684.5,
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"wilcoxon_p": 0.01329956259887278,
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"significant_at_0.05": true
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},
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"9B_AdamW_step200_vs_4B_Muon_step200": {
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"mean_diff": 6.778,
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"bootstrap_ci95": [
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4.832,
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8.766
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],
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"wilcoxon_W": 4612.5,
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"wilcoxon_p": 9.92502554694572e-10,
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"significant_at_0.05": true
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},
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"4B_AdamW_step200_vs_4B_Muon_step200": {
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"mean_diff": 4.499,
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"bootstrap_ci95": [
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2.49,
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6.481
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],
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"wilcoxon_W": 5987.5,
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"wilcoxon_p": 2.1404346737721187e-05,
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"significant_at_0.05": true
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}
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}
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