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

Libraries

How to use DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF",
	filename="Llama-3.2-Taiwan-3B-Instruct-Q5_K_M.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 DavidLanz/Llama-3.2-Taiwan-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 DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_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 DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_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 DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_K_M

Use Docker

docker model run hf.co/DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_K_M

LM Studio
Jan

vLLM

How to use DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DavidLanz/Llama-3.2-Taiwan-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": "DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_K_M

Ollama
How to use DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_K_M
```

Unsloth Studio

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

How to use DavidLanz/Llama-3.2-Taiwan-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 DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_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": "DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DavidLanz/Llama-3.2-Taiwan-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 DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_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 DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_K_M

Run Hermes

hermes

Docker Model Runner
How to use DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF:Q5_K_M

Run and chat with the model

lemonade run user.Llama-3.2-Taiwan-3B-Instruct-GGUF-Q5_K_M

List all available models

lemonade list

A newer version of this model is available: DavidLanz/Llama-3.2-Taiwan-3B-Instruct

Model Card for DavidLanz/Llama-3.2-Taiwan-3B-Instruct-GGUF

圖像後製來自 VisuaParadigm：站在玉山上的量化 🦙 。

透過 llama.cpp 將 DavidLanz/Llama-3.2-Taiwan-3B-Instruct 版本轉成 .gguf 和各種量化版本模型。

Model Change Log

Update Date	Model Version	Key Changes
2025-01-22	v2025.01.01	This version corresponds to the `v2025.01.22` release of DavidLanz/Llama-3.2-Taiwan-3B-Instruct.
2025-01-01	v2025.01.01	This version corresponds to the `v2025.01.01` release of DavidLanz/Llama-3.2-Taiwan-3B-Instruct.
2024-12-11	v2024.12.11	This version corresponds to the `v2024.11.27` release of DavidLanz/Llama-3.2-Taiwan-3B-Instruct.

More Information

請參考不同 tag 選擇對照的原始非量化的版本，最新的 main 分支對映的是 v2025.01.01 版本，有關原始非量化版本請參考原始模型 DavidLanz/Llama-3.2-Taiwan-3B-Instruct 介紹。

已知問題： 量化後的模型會有機率輸出全部簡體中文的情況，此問題目前尚未深入研究原因。

Issuses

How to use in Ollama

根據 Issue：ollama 直接run gguf會跑出文不對題討論串， @k1dave6412 查出原因是要調整預設的對話模板（chat template），故我們在 repo 內有放置一個 template 的檔案來修正這個問題，但如果你要客制你的對話模板，請照著 Ollama 的 Template 設定。