Instructions to use lucas-sousa-pereira/granite-3-3-8b-java-code-refactoring with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use lucas-sousa-pereira/granite-3-3-8b-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/granite-3-3-8b-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/granite-3-3-8b-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/granite-3-3-8b-java-code-refactoring:Q4_K_M # Run inference directly in the terminal: llama cli -hf lucas-sousa-pereira/granite-3-3-8b-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/granite-3-3-8b-java-code-refactoring:Q4_K_M # Run inference directly in the terminal: llama cli -hf lucas-sousa-pereira/granite-3-3-8b-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/granite-3-3-8b-java-code-refactoring:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf lucas-sousa-pereira/granite-3-3-8b-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/granite-3-3-8b-java-code-refactoring:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf lucas-sousa-pereira/granite-3-3-8b-java-code-refactoring:Q4_K_M
Use Docker
docker model run hf.co/lucas-sousa-pereira/granite-3-3-8b-java-code-refactoring:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use lucas-sousa-pereira/granite-3-3-8b-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/granite-3-3-8b-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/granite-3-3-8b-java-code-refactoring", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/lucas-sousa-pereira/granite-3-3-8b-java-code-refactoring:Q4_K_M
- Ollama
How to use lucas-sousa-pereira/granite-3-3-8b-java-code-refactoring with Ollama:
ollama run hf.co/lucas-sousa-pereira/granite-3-3-8b-java-code-refactoring:Q4_K_M
- Unsloth Studio
How to use lucas-sousa-pereira/granite-3-3-8b-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/granite-3-3-8b-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/granite-3-3-8b-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/granite-3-3-8b-java-code-refactoring to start chatting
- Pi
How to use lucas-sousa-pereira/granite-3-3-8b-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/granite-3-3-8b-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/granite-3-3-8b-java-code-refactoring:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use lucas-sousa-pereira/granite-3-3-8b-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/granite-3-3-8b-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/granite-3-3-8b-java-code-refactoring:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use lucas-sousa-pereira/granite-3-3-8b-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/granite-3-3-8b-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/granite-3-3-8b-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/granite-3-3-8b-java-code-refactoring with Docker Model Runner:
docker model run hf.co/lucas-sousa-pereira/granite-3-3-8b-java-code-refactoring:Q4_K_M
- Lemonade
How to use lucas-sousa-pereira/granite-3-3-8b-java-code-refactoring with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull lucas-sousa-pereira/granite-3-3-8b-java-code-refactoring:Q4_K_M
Run and chat with the model
lemonade run user.granite-3-3-8b-java-code-refactoring-Q4_K_M
List all available models
lemonade list
Update README.md
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---
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license: apache-2.0
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---
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license: apache-2.0
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base_model: ibm-granite/granite-3.3-8b-instruct
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language:
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- en
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tags:
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- instructlab
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- lora
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- code
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- java
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- code-refactoring
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- granite
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pipeline_tag: text-generation
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---
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# Granite-3.3-8B-Instruct Fine-Tuned for Java Code Refactoring
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## Overview
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This model is a fine-tuned version of **IBM Granite-3.3-8B-Instruct** specialized for **Java code refactoring** using the **InstructLab** framework.
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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.
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## Base Model
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- ibm-granite/granite-3.3-8b-instruct
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## Training
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The dataset was generated using:
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```bash
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ilab data generate \
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--model "openai/gpt-4o" \
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--endpoint-url "http://localhost:8000/v1" \
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--api-key "$ILAB_API_KEY" \
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--pipeline simple
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```
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The model was fine-tuned using:
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```bash
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ilab model train \
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--model-path ibm-granite/granite-3.3-8b-instruct \
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--num-epochs 3 \
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--device cpu \
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--max-seq-len 2048 \
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--max-batch-len 10000 \
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--effective-batch-size 128 \
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--lora-rank 32 \
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--lora-alpha 64 \
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--lora-dropout 0.1 \
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--learning-rate 1e-4 \
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--is-padding-free false
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```
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## Intended Use
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This model is intended for Java code refactoring tasks. It is designed to generate refactored Java code while preserving the original program behavior.
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## Training Data
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- Framework: InstructLab
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- Pipeline: Skills
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- Teacher model: GPT-4o
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- Number of training instances: 1,440
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## License
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This model is released under the Apache 2.0 license, consistent with the license of the base model.
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