CodeAid/CouplingSmells-Detection-Data
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How to use CodeAid/CouplingSmells-Detection-model with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="CodeAid/CouplingSmells-Detection-model") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("CodeAid/CouplingSmells-Detection-model")
model = AutoModelForCausalLM.from_pretrained("CodeAid/CouplingSmells-Detection-model")How to use CodeAid/CouplingSmells-Detection-model with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeAid/CouplingSmells-Detection-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "CodeAid/CouplingSmells-Detection-model",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/CodeAid/CouplingSmells-Detection-model
How to use CodeAid/CouplingSmells-Detection-model with SGLang:
# 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
}'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
}'How to use CodeAid/CouplingSmells-Detection-model with Docker Model Runner:
docker model run hf.co/CodeAid/CouplingSmells-Detection-model
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.
The model identifies coupling-related code smells such as:
It analyzes Java classes and their dependencies to detect architectural or design issues that increase coupling and reduce maintainability.
safetensors (merged)