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 designcoder_evaluator_qwen3.5_9b_adamw_bs256_data37847_step100/processor_config.json
Browse files
designcoder_evaluator_qwen3.5_9b_adamw_bs256_data37847_step100/processor_config.json
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{
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"image_processor": {
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"do_convert_rgb": true,
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"image_processor_type": "Qwen2VLImageProcessor",
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"image_std": [
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"merge_size": 2,
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"patch_size": 16,
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"longest_edge": 16777216,
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"shortest_edge": 65536
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},
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"temporal_patch_size": 2
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},
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"processor_class": "Qwen3VLProcessor",
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"video_processor": {
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"do_convert_rgb": true,
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"do_sample_frames": true,
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"fps": 2,
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"image_mean": [
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"max_frames": 768,
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"merge_size": 2,
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"min_frames": 4,
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"patch_size": 16,
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"return_metadata": false,
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"size": {
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"longest_edge": 25165824,
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"shortest_edge": 4096
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},
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"temporal_patch_size": 2,
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"video_processor_type": "Qwen3VLVideoProcessor"
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}
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}
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