CodeAid/CouplingSmells-Detection-Data
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How to use codeaidbackUp/OldCouplingSmellsDetectionModel with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="codeaidbackUp/OldCouplingSmellsDetectionModel")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("codeaidbackUp/OldCouplingSmellsDetectionModel")
model = AutoModelForCausalLM.from_pretrained("codeaidbackUp/OldCouplingSmellsDetectionModel")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))How to use codeaidbackUp/OldCouplingSmellsDetectionModel with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "codeaidbackUp/OldCouplingSmellsDetectionModel"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "codeaidbackUp/OldCouplingSmellsDetectionModel",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/codeaidbackUp/OldCouplingSmellsDetectionModel
How to use codeaidbackUp/OldCouplingSmellsDetectionModel with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "codeaidbackUp/OldCouplingSmellsDetectionModel" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "codeaidbackUp/OldCouplingSmellsDetectionModel",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "codeaidbackUp/OldCouplingSmellsDetectionModel" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "codeaidbackUp/OldCouplingSmellsDetectionModel",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use codeaidbackUp/OldCouplingSmellsDetectionModel with Docker Model Runner:
docker model run hf.co/codeaidbackUp/OldCouplingSmellsDetectionModel
docker model run hf.co/codeaidbackUp/OldCouplingSmellsDetectionModelThis 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)
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "codeaidbackUp/OldCouplingSmellsDetectionModel"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codeaidbackUp/OldCouplingSmellsDetectionModel", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'