How to use from
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 "CodeAid/CouplingSmells-Detection-model" \
    --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": "CodeAid/CouplingSmells-Detection-model",
		"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 "CodeAid/CouplingSmells-Detection-model" \
        --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": "CodeAid/CouplingSmells-Detection-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Quick Links

CodeAid Coupling Smells Detection Model (Qwen2.5-14B-Instruct Fine-Tuned)

This model is a fine-tuned version of Qwen2.5-14B-Instruct, specialized for detecting coupling smells in Java code. It was developed as part of the CodeAid project to assist developers in identifying code quality issues directly in their IDE.

🧠 Model Purpose

The model identifies coupling-related code smells such as:

  • Feature Envy
  • Inappropriate Intimacy
  • Message Chains
  • Excessive Dependencies

It analyzes Java classes and their dependencies to detect architectural or design issues that increase coupling and reduce maintainability.

πŸ”§ Technical Details

  • Base Model: Qwen2.5-14B-Instruct
  • Fine-Tuning Method: QLoRA with LoRA adapters merged
  • Format: safetensors (merged)
  • Task Type: Text generation (instruction-based)
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Dataset used to train CodeAid/CouplingSmells-Detection-model