Instructions to use bartowski/Llama-3.2-3B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bartowski/Llama-3.2-3B-Instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartowski/Llama-3.2-3B-Instruct-GGUF", filename="Llama-3.2-3B-Instruct-IQ3_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 bartowski/Llama-3.2-3B-Instruct-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/Llama-3.2-3B-Instruct-GGUF: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 bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/Llama-3.2-3B-Instruct-GGUF: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 bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/Llama-3.2-3B-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/Llama-3.2-3B-Instruct-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/Llama-3.2-3B-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
- Ollama
How to use bartowski/Llama-3.2-3B-Instruct-GGUF with Ollama:
ollama run hf.co/bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
- Unsloth Studio
How to use bartowski/Llama-3.2-3B-Instruct-GGUF 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 bartowski/Llama-3.2-3B-Instruct-GGUF 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 bartowski/Llama-3.2-3B-Instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bartowski/Llama-3.2-3B-Instruct-GGUF to start chatting
- Pi
How to use bartowski/Llama-3.2-3B-Instruct-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bartowski/Llama-3.2-3B-Instruct-GGUF: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": "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bartowski/Llama-3.2-3B-Instruct-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bartowski/Llama-3.2-3B-Instruct-GGUF: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 bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use bartowski/Llama-3.2-3B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
- Lemonade
How to use bartowski/Llama-3.2-3B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Llama-3.2-3B-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
Chat template
Hi, pretty new on this thing, not sure what to do with the "Prompt format" topic, to use as chat should I need to provide a different chat template them the usual for Llama-3.1 (is what I'm doing now)
By the way, amazing work, congrats!!
llama 3.2 uses the same base prompt format as 3 and 3.1, It just has extra features, such as the "<|image|>" tag if using the 11B vision. How you choose to load and inject information into the final prompt depends a lot on your method, so I would suggest just to find llama 3.1 (or 3) examples for whatever program/language you use.
The prompt format is basically what your final cleaned up "prompt" should look like. It allows the model to classify the type of inputs/outputs. I'll post an example from SillyTavern below for how a basic conversation could look like.
This is basically saying: START -> "system" said _blank -> "assistant" said _blank -> "user" said _blank -> etc...
SNIPPET:
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are an example character. Behave like an AI assistant.<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Hello!<|eot_id|><|start_header_id|>user<|end_header_id|>
high there<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Hello! How can I assist you today? Is it help with something specific or general chat that you're in the mood for? I'm here to listen and provide information on a wide range of topics.<|eot_id|><|start_header_id|>user<|end_header_id|>
Say hi 5 times.<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Hi, hi, hi, hi, Hi!<|eot_id|><|start_header_id|>user<|end_header_id|>
SNIPPET#2 (commandline):
Input: {"prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nAct as bee....
Perfect thanks!!