Instructions to use codeaidbackUp/OldCouplingSmellsDetectionModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use codeaidbackUp/OldCouplingSmellsDetectionModel with vLLM:

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?"
			}
		]
	}'

Use Docker

docker model run hf.co/codeaidbackUp/OldCouplingSmellsDetectionModel

SGLang

How to use codeaidbackUp/OldCouplingSmellsDetectionModel 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 "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?"
			}
		]
	}'

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 "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 Model Runner
How to use codeaidbackUp/OldCouplingSmellsDetectionModel with Docker Model Runner:
```
docker model run hf.co/codeaidbackUp/OldCouplingSmellsDetectionModel
```

OldCouplingSmellsDetectionModel / README.md

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	---
	license: apache-2.0
	model_type: qwen
	datasets:
	- CodeAid/CouplingDetectionData
	language:
	- en
	base_model:
	- Qwen/Qwen2.5-14B-Instruct
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- llms
	- code
	- Java
	- code-smells
	- transformers
	---

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

	This model is a fine-tuned version of [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/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.

	## 🔧 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)