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
Add full V5.9.2 evaluation artifacts
Browse files- eval/rubric_stats.json +58 -25
eval/rubric_stats.json
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"Components": 1517,
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"Assets": 584,
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"Aesthetics": 782
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"A/dashboard": 1000,
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"A/landing": 2486,
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"B/dashboard": 748,
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"B/landing": 749
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"all_check_with_screenshot": true,
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"note": "Additionally every prompt carries 5 judge-side Prompt-Fit rubrics scored 0-2 (surface-specific), not part of the 23-25 frozen screenshot checks."
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}
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{
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"frozen_per_case": {
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"n_prompts": 200,
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"checks_total": 4983,
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"per_prompt": {
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"min": 23,
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"max": 25,
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"mean": 24.91
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},
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"distribution": {
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"23": 1,
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"24": 15,
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"25": 184
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},
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"by_dimension": {
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"Alignment": 616,
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"Layout": 842,
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"Typography": 642,
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"Components": 1517,
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"Assets": 584,
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"Aesthetics": 782
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},
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"by_track_surface": {
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"A/dashboard": 1000,
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"A/landing": 2486,
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"B/dashboard": 748,
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"B/landing": 749
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},
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"scale": "binary 0/1",
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"all_check_with_screenshot": true
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"fixed_per_surface": {
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"landing": {
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"prompt_fit": 5,
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"defect_checks": 27,
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"detail_checks": 8,
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"static_dimensions": [
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"Layout & Composition",
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"Typography & Readability",
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"Component & Interaction Design",
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"Assets & Semantic Fit",
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"Visual System Design"
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]
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},
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"dashboard": {
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"prompt_fit": 5,
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"defect_checks": 25,
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"detail_checks": 9,
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"static_dimensions": [
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"Layout & Composition",
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"scales": {
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"prompt_fit": "0/1/2",
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"defect_checks": "2 clean, 0 fail, N/A when inapplicable",
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"detail_checks": "0/1/2, N/A when inapplicable"
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"aggregation": "Unweighted mean of top-level slots: Prompt Fit (1 slot) + each active static dimension (1 slot each) + the whole Frozen family (1 slot, itself the equal mean of its per-dimension means)."
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}
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