Instructions to use ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf", filename="Taiwan-inquiry_7B_v2.0-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf ChenWeiLi/Taiwan-inquiry_7B_v2.0.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 ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf ChenWeiLi/Taiwan-inquiry_7B_v2.0.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 ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ChenWeiLi/Taiwan-inquiry_7B_v2.0.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 ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf:Q4_K_M
Use Docker
docker model run hf.co/ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf with Ollama:
ollama run hf.co/ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf:Q4_K_M
- Unsloth Studio
How to use ChenWeiLi/Taiwan-inquiry_7B_v2.0.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 ChenWeiLi/Taiwan-inquiry_7B_v2.0.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 ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf to start chatting
- Atomic Chat new
- Docker Model Runner
How to use ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf with Docker Model Runner:
docker model run hf.co/ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf:Q4_K_M
- Lemonade
How to use ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ChenWeiLi/Taiwan-inquiry_7B_v2.0.gguf:Q4_K_M
Run and chat with the model
lemonade run user.Taiwan-inquiry_7B_v2.0.gguf-Q4_K_M
List all available models
lemonade list
- Xet hash:
- a13d0ac6a334fd10ff7793cb1cdeb2cbb2a3ccc307422e0538e8422e4bc9a461
- Size of remote file:
- 7.96 GB
- SHA256:
- d4ae060ee5790f1ef7e7f981f9040220660917bde34b29d0dea54b4b8cc1f5e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.