Instructions to use merterbak/Llama-3.2-3B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use merterbak/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="merterbak/Llama-3.2-3B-Instruct-GGUF",
	filename="Llama-3.2-3B-Instruct-Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use merterbak/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 merterbak/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf merterbak/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 merterbak/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf merterbak/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 merterbak/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf merterbak/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 merterbak/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf merterbak/Llama-3.2-3B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/merterbak/Llama-3.2-3B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use merterbak/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 "merterbak/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": "merterbak/Llama-3.2-3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/merterbak/Llama-3.2-3B-Instruct-GGUF:Q4_K_M

Ollama
How to use merterbak/Llama-3.2-3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/merterbak/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use merterbak/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 merterbak/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 merterbak/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 merterbak/Llama-3.2-3B-Instruct-GGUF to start chatting

Pi new

How to use merterbak/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 merterbak/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": "merterbak/Llama-3.2-3B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use merterbak/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 merterbak/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 merterbak/Llama-3.2-3B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use merterbak/Llama-3.2-3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/merterbak/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use merterbak/Llama-3.2-3B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull merterbak/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

Llama 3.2 3B Instruct model available in multiple GGUF quantization formats, also stored on Xet for fast and efficient access.

Original model: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct

Model Information

The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.

Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Downloads last month: 126

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for merterbak/Llama-3.2-3B-Instruct-GGUF

Base model

meta-llama/Llama-3.2-3B-Instruct

Quantized

(453)

this model