Instructions to use yun2022/linux-meta-llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use yun2022/linux-meta-llama 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 yun2022/linux-meta-llama # Run inference directly in the terminal: llama cli -hf yun2022/linux-meta-llama
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf yun2022/linux-meta-llama # Run inference directly in the terminal: llama cli -hf yun2022/linux-meta-llama
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 yun2022/linux-meta-llama # Run inference directly in the terminal: ./llama-cli -hf yun2022/linux-meta-llama
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 yun2022/linux-meta-llama # Run inference directly in the terminal: ./build/bin/llama-cli -hf yun2022/linux-meta-llama
Use Docker
docker model run hf.co/yun2022/linux-meta-llama
- Llama new
- LM Studio
- Jan
- Ollama
How to use yun2022/linux-meta-llama with Ollama:
ollama run hf.co/yun2022/linux-meta-llama
- Unsloth Desktop
- Docker Model Runner
How to use yun2022/linux-meta-llama with Docker Model Runner:
docker model run hf.co/yun2022/linux-meta-llama
- Lemonade
How to use yun2022/linux-meta-llama with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull yun2022/linux-meta-llama
Run and chat with the model
lemonade run user.linux-meta-llama-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Download tokenizer.json from yun2022/linux-meta-llama: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/yun2022/linux-meta-llama/resolve/main/tokenizer.json
- Command line
-
hf download hf://yun2022/linux-meta-llama/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/yun2022/linux-meta-llama/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- 2e56cad9ac2a38f4b7de211953881b613c19e51838c05adf8f7984c139c2e8f5
- Size of remote file:
- 17.2 MB
- SHA256:
- 6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
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