Traditional Chinese Data and Models
Collection
繁體中文(台灣)資料集與模型:合成資料蒸餾、DRCD QA、醫療選擇題微調,皆附配對統計與授權邊界。 • 12 items • Updated
How to use steven0226/llama-3.1-8b-taiwan-chat-gguf with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M
# 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 steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M
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 steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M
docker model run hf.co/steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M
How to use steven0226/llama-3.1-8b-taiwan-chat-gguf with Ollama:
ollama run hf.co/steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M
How to use steven0226/llama-3.1-8b-taiwan-chat-gguf with Pi:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M
# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"llama-cpp": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M"
}
]
}
}
}# Start Pi in your project directory: pi
How to use steven0226/llama-3.1-8b-taiwan-chat-gguf with Docker Model Runner:
docker model run hf.co/steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M
How to use steven0226/llama-3.1-8b-taiwan-chat-gguf with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M
lemonade run user.llama-3.1-8b-taiwan-chat-gguf-Q4_K_M
lemonade list
How to use steven0226/llama-3.1-8b-taiwan-chat-gguf with Hermes Agent:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M
# 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 steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M
hermes
How to use steven0226/llama-3.1-8b-taiwan-chat-gguf with OpenClaw:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M
# 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 "steven0226/llama-3.1-8b-taiwan-chat-gguf:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
openclaw agent --local --agent main --message "Hello from Hugging Face"
Built with Llama
GGUF 量化版本(q4_k_m),轉換自 steven0226/llama-3.1-8b-taiwan-chat——訓練細節、資料集、超參數、微調前後對照,請見合併模型 repo 的完整 model card。
可直接用 Ollama、LM Studio、llama.cpp 載入本 repo 內的 .gguf 檔。
4-bit
Base model
meta-llama/Llama-3.1-8B