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
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| "content": "<|start_of_role|>", | |
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| "rstrip": false, | |
| "single_word": false, | |
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| "content": "<|tool_call|>", | |
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| "49155": { | |
| "content": "<|start_of_cite|>", | |
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| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
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| "49156": { | |
| "content": "<|end_of_cite|>", | |
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| "content": "<|end_of_plugin|>", | |
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| "additional_special_tokens": [ | |
| "<|start_of_role|>", | |
| "<|end_of_role|>", | |
| "<|tool_call|>", | |
| "<|start_of_cite|>", | |
| "<|end_of_cite|>", | |
| "<|start_of_plugin|>", | |
| "<|end_of_plugin|>", | |
| "<|end_of_text|>", | |
| "<|end_of_text|>" | |
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| "bos_token": "<|end_of_text|>", | |
| "chat_template": "{# Alias tools -> available_tools #}\n{%- if tools and not available_tools -%}\n {%- set available_tools = tools -%}\n{%- endif -%}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n {%- else %}\n {%- set system_message = \"Knowledge Cutoff Date: April 2024.\nToday's Date: \" + strftime_now('%B %d, %Y') + \".\nYou are Granite, developed by IBM.\" %}\n {%- if available_tools and documents %}\n {%- set system_message = system_message + \" You are a helpful assistant with access to the following tools. When a tool is required to answer the user's query, respond only with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.\nWrite the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data.\" %}\n {%- elif available_tools %}\n {%- set system_message = system_message + \" You are a helpful assistant with access to the following tools. When a tool is required to answer the user's query, respond only with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.\" %}\n {%- elif documents %}\n {%- set system_message = system_message + \" Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data.\" %}\n {%- elif thinking %}\n {%- set system_message = system_message + \" You are a helpful AI assistant.\nRespond to every user query in a comprehensive and detailed way. You can write down your thoughts and reasoning process before responding. In the thought process, engage in a comprehensive cycle of analysis, summarization, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. In the response section, based on various attempts, explorations, and reflections from the thoughts section, systematically present the final solution that you deem correct. The response should summarize the thought process. Write your thoughts between <think></think> and write your response between <response></response> for each user query.\" %}\n {%- else %}\n {%- set system_message = system_message + \" You are a helpful AI assistant.\" %}\n {%- endif %}\n {%- if 'citations' in controls and documents %}\n {%- set system_message = system_message + '\nUse the symbols <|start_of_cite|> and <|end_of_cite|> to indicate when a fact comes from a document in the search result, e.g <|start_of_cite|> {document_id: 1}my fact <|end_of_cite|> for a fact from document 1. Afterwards, list all the citations with their corresponding documents in an ordered list.' %}\n {%- endif %}\n {%- if 'hallucinations' in controls and documents %}\n {%- set system_message = system_message + '\nFinally, after the response is written, include a numbered list of sentences from the response with a corresponding risk value that are hallucinated and not based in the documents.' %}\n {%- endif %}\n {%- set loop_messages = messages %}\n {%- endif %}\n {{- '<|start_of_role|>system<|end_of_role|>' + system_message + '<|end_of_text|>\n' }}\n {%- if available_tools %}\n {{- '<|start_of_role|>available_tools<|end_of_role|>' }}\n {{- available_tools | tojson(indent=4) }}\n {{- '<|end_of_text|>\n' }}\n {%- endif %}\n {%- if documents %}\n {%- for document in documents %}\n {{- '<|start_of_role|>document {\"document_id\": \"' + document['doc_id'] | string + '\"}<|end_of_role|>\n' }}\n {{- document['text'] }}\n {{- '<|end_of_text|>\n' }}\n {%- endfor %}\n {%- endif %}\n {%- for message in loop_messages %}\n {{- '<|start_of_role|>' + message['role'] + '<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- if loop.last and add_generation_prompt %}\n {{- '<|start_of_role|>assistant' }}\n {%- if controls %}\n {{- ' ' + controls | tojson()}}\n {%- endif %}\n {{- '<|end_of_role|>' }}\n {%- endif %}\n {%- endfor %}", | |
| "clean_up_tokenization_spaces": true, | |
| "eos_token": "<|end_of_text|>", | |
| "errors": "replace", | |
| "extra_special_tokens": {}, | |
| "fast_tokenizer": true, | |
| "model_max_length": 9223372036854775807, | |
| "pad_token": "<|end_of_text|>", | |
| "padding_side": "left", | |
| "tokenizer_class": "GPT2Tokenizer", | |
| "unk_token": "<|end_of_text|>", | |
| "vocab_size": 49152 | |
| } | |