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/trainer_log.jsonl
Browse files
designcoder_evaluator_qwen3.5_9b_adamw_bs256_data37847_step100/trainer_log.jsonl
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{"current_steps": 10, "total_steps": 296, "loss": 1.327353572845459, "lr": 3e-06, "epoch": 0.06756756756756757, "percentage": 3.38, "elapsed_time": "0:11:35", "remaining_time": "5:31:35"}
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{"current_steps": 20, "total_steps": 296, "loss": 0.9504005432128906, "lr": 6.333333333333333e-06, "epoch": 0.13513513513513514, "percentage": 6.76, "elapsed_time": "0:19:17", "remaining_time": "4:26:07"}
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{"current_steps": 30, "total_steps": 296, "loss": 0.7079431533813476, "lr": 9.666666666666667e-06, "epoch": 0.20270270270270271, "percentage": 10.14, "elapsed_time": "0:24:26", "remaining_time": "3:36:41"}
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{"current_steps": 40, "total_steps": 296, "loss": 0.6138272285461426, "lr": 9.971780289496585e-06, "epoch": 0.2702702702702703, "percentage": 13.51, "elapsed_time": "0:28:32", "remaining_time": "3:02:39"}
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{"current_steps": 50, "total_steps": 296, "loss": 0.567446231842041, "lr": 9.874639560909118e-06, "epoch": 0.33783783783783783, "percentage": 16.89, "elapsed_time": "0:32:49", "remaining_time": "2:41:31"}
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{"current_steps": 60, "total_steps": 296, "loss": 0.539431095123291, "lr": 9.70958251970347e-06, "epoch": 0.40540540540540543, "percentage": 20.27, "elapsed_time": "0:38:16", "remaining_time": "2:30:34"}
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{"current_steps": 70, "total_steps": 296, "loss": 0.5228753566741944, "lr": 9.478908836546629e-06, "epoch": 0.47297297297297297, "percentage": 23.65, "elapsed_time": "0:41:35", "remaining_time": "2:14:16"}
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{"current_steps": 80, "total_steps": 296, "loss": 0.5104542732238769, "lr": 9.185832391312644e-06, "epoch": 0.5405405405405406, "percentage": 27.03, "elapsed_time": "0:45:17", "remaining_time": "2:02:16"}
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{"current_steps": 90, "total_steps": 296, "loss": 0.5040336608886719, "lr": 8.834436495439588e-06, "epoch": 0.6081081081081081, "percentage": 30.41, "elapsed_time": "0:48:25", "remaining_time": "1:50:50"}
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{"current_steps": 100, "total_steps": 296, "loss": 0.49561896324157717, "lr": 8.429617000860441e-06, "epoch": 0.6756756756756757, "percentage": 33.78, "elapsed_time": "0:51:25", "remaining_time": "1:40:47"}
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