Instructions to use lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring",
	filename="pytorch_model-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M

Use Docker

docker model run hf.co/lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M

LM Studio
Jan

vLLM

How to use lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M

Ollama
How to use lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring with Ollama:
```
ollama run hf.co/lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M
```

Unsloth Studio

How to use lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring to start chatting

How to use lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring with Docker Model Runner:
```
docker model run hf.co/lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M
```

Lemonade

How to use lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lucas-sousa-pereira/qwen2.5-7b-java-code-refactoring:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-7b-java-code-refactoring-Q4_K_M

List all available models

lemonade list

Qwen2.5-7B-Instruct Fine-Tuned for Java Code Refactoring

Overview

This model is a fine-tuned version of Qwen2.5-7B-Instruct specialized for Java code refactoring using the InstructLab framework.

The training dataset was generated using the Skills pipeline of InstructLab, with GPT-4o acting as the teacher model to generate instruction-response pairs. A total of 1,440 synthetic training instances were used for fine-tuning.

Base Model

Qwen/Qwen2.5-7B-Instruct

Training

The dataset was generated using:

ilab data generate \
  --model "openai/gpt-4o" \
  --endpoint-url "http://localhost:8000/v1" \
  --api-key "$ILAB_API_KEY" \
  --pipeline simple

The model was fine-tuned using:

ilab model train \
  --model-path Qwen/Qwen2.5-7B-Instruct \
  --num-epochs 3 \
  --device cpu \
  --max-seq-len 2048 \
  --max-batch-len 10000 \
  --effective-batch-size 128 \
  --lora-rank 32 \
  --lora-alpha 64 \
  --lora-dropout 0.1 \
  --learning-rate 1e-4 \
  --is-padding-free false

Intended Use

This model is intended for Java code refactoring tasks. It is designed to generate refactored Java code while preserving the original program behavior.