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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use DevQuasar/Llama-3.2-3B-GGUF 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 DevQuasar/Llama-3.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DevQuasar/Llama-3.2-3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DevQuasar/Llama-3.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DevQuasar/Llama-3.2-3B-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 DevQuasar/Llama-3.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DevQuasar/Llama-3.2-3B-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 DevQuasar/Llama-3.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DevQuasar/Llama-3.2-3B-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use DevQuasar/Llama-3.2-3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DevQuasar/Llama-3.2-3B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DevQuasar/Llama-3.2-3B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

Ollama
How to use DevQuasar/Llama-3.2-3B-GGUF with Ollama:
```
ollama run hf.co/DevQuasar/Llama-3.2-3B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use DevQuasar/Llama-3.2-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/DevQuasar/Llama-3.2-3B-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DevQuasar/Llama-3.2-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.2-3B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

I'm doing this to 'Make knowledge free for everyone', using my personal time and resources.

If you want to support my efforts please visit my ko-fi page: https://ko-fi.com/devquasar

Also feel free to visit my website https://devquasar.com/

Downloads last month: 109

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for DevQuasar/Llama-3.2-3B-GGUF

Base model

meta-llama/Llama-3.2-3B

Quantized

(157)

this model

Collection including DevQuasar/Llama-3.2-3B-GGUF

GGUFs

Collection

quantized models • 234 items • Updated Mar 2 • 5