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
| { | |
| "frozen_per_case": { | |
| "n_prompts": 200, | |
| "checks_total": 4983, | |
| "per_prompt": { | |
| "min": 23, | |
| "max": 25, | |
| "mean": 24.91 | |
| }, | |
| "distribution": { | |
| "23": 1, | |
| "24": 15, | |
| "25": 184 | |
| }, | |
| "by_dimension": { | |
| "Alignment": 616, | |
| "Layout": 842, | |
| "Typography": 642, | |
| "Components": 1517, | |
| "Assets": 584, | |
| "Aesthetics": 782 | |
| }, | |
| "by_track_surface": { | |
| "A/dashboard": 1000, | |
| "A/landing": 2486, | |
| "B/dashboard": 748, | |
| "B/landing": 749 | |
| }, | |
| "scale": "binary 0/1", | |
| "all_check_with_screenshot": true | |
| }, | |
| "fixed_per_surface": { | |
| "landing": { | |
| "prompt_fit": 5, | |
| "defect_checks": 27, | |
| "detail_checks": 8, | |
| "static_dimensions": [ | |
| "Layout & Composition", | |
| "Typography & Readability", | |
| "Component & Interaction Design", | |
| "Assets & Semantic Fit", | |
| "Visual System Design" | |
| ] | |
| }, | |
| "dashboard": { | |
| "prompt_fit": 5, | |
| "defect_checks": 25, | |
| "detail_checks": 9, | |
| "static_dimensions": [ | |
| "Layout & Composition", | |
| "Typography & Readability", | |
| "Component & Interaction Design", | |
| "Visual System Design" | |
| ] | |
| } | |
| }, | |
| "scales": { | |
| "prompt_fit": "0/1/2", | |
| "defect_checks": "2 clean, 0 fail, N/A when inapplicable", | |
| "detail_checks": "0/1/2, N/A when inapplicable" | |
| }, | |
| "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)." | |
| } |