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
Commit History
29 days ago
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Update bench-200 final full-run scores (n=200 all models) + eval artifacts
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30 days ago
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Update model card: add 4 data41287 checkpoints, fix dataset revision, add bench-200 scores
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Add designcoder_qwen3.8_27b_adamw_bs128_data41287_step400 (bench-200: 91.19, strongest in release)
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Add designcoder_qwen3.5_9b_adamw_bs256_data41287_step200 (bench-200: 84.40)
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Add designcoder_qwen3.5_4b_muon_bs256_data41287_step200 (bench-200: 83.36)
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Add designcoder_qwen3.5_4b_adamw_bs256_data41287_step200 (bench-200: 84.22)
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Update model card: drop removed 9B ep20 arm
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Remove 9B ep20 over-training arm
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Update model card: drop removed 27B arm, add weight decay column, generalize naming rule
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Remove 27B wd=0.05 arm (no eval data at step1900; v1 wins at comparable step 1600)
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Add model card: naming convention and checkpoint index
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Add designcoder_qwen3.6_27b_adamw_bs32_step1900_r2 (base Qwen3.6-27B, AdamW, lr1e-5, global bs32, 2 epochs, step 1900, rerun 2)
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Add designcoder_qwen3.6_27b_adamw_bs32_step1900 (base Qwen3.6-27B, AdamW, lr1e-5, global bs32, 2 epochs, step 1900)
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Add designcoder_qwen3.5_9b_adamw_bs16_ep20_step38000 (base Qwen3.5-9B, AdamW, lr2e-5, global bs16, 20 epochs, step 38000)
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Add training log for 9b muon run
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Add designcoder_qwen3.5_9b_muon_bs16_step3800 (base Qwen3.5-9B, Muon, lr1e-5, global bs16, 2 epochs, step 3800)
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Add designcoder_qwen3.5_9b_adamw_bs16_step3800 (base Qwen3.5-9B, AdamW, lr2e-5, global bs16, 2 epochs, step 3800)
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Add designcoder_qwen3.5_4b_muon_bs32_step1900 (base Qwen3.5-4B, Muon, lr1e-5, global bs32, 2 epochs, step 1900)
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initial commit
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