Instructions to use Felipe97/llama-cpp-compiled 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 Felipe97/llama-cpp-compiled 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 Felipe97/llama-cpp-compiled # Run inference directly in the terminal: llama cli -hf Felipe97/llama-cpp-compiled
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Felipe97/llama-cpp-compiled # Run inference directly in the terminal: llama cli -hf Felipe97/llama-cpp-compiled
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 Felipe97/llama-cpp-compiled # Run inference directly in the terminal: ./llama-cli -hf Felipe97/llama-cpp-compiled
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 Felipe97/llama-cpp-compiled # Run inference directly in the terminal: ./build/bin/llama-cli -hf Felipe97/llama-cpp-compiled
Use Docker
docker model run hf.co/Felipe97/llama-cpp-compiled
- LM Studio
- Jan
- Ollama
How to use Felipe97/llama-cpp-compiled with Ollama:
ollama run hf.co/Felipe97/llama-cpp-compiled
- Unsloth Desktop
- Docker Model Runner
How to use Felipe97/llama-cpp-compiled with Docker Model Runner:
docker model run hf.co/Felipe97/llama-cpp-compiled
- Lemonade
How to use Felipe97/llama-cpp-compiled with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Felipe97/llama-cpp-compiled
Run and chat with the model
lemonade run user.llama-cpp-compiled-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
| static void signal_handler(int) { | |
| if (cli_context::interrupted().load()) { | |
| // second Ctrl+C - exit immediately | |
| // make sure to clear colors before exiting (not using LOG or console.cpp here to avoid deadlock) | |
| fprintf(stdout, "\033[0m\n"); | |
| fflush(stdout); | |
| std::exit(130); | |
| } | |
| cli_context::interrupted().store(true); | |
| } | |
| // satisfies -Wmissing-declarations | |
| int llama_cli(int argc, char ** argv); | |
| int llama_cli(int argc, char ** argv) { | |
| common_params params; | |
| params.verbosity = LOG_LEVEL_ERROR; // by default, less verbose logs | |
| common_init(); | |
| if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_CLI)) { | |
| return 1; | |
| } | |
| struct sigaction sigint_action; | |
| sigint_action.sa_handler = signal_handler; | |
| sigemptyset (&sigint_action.sa_mask); | |
| sigint_action.sa_flags = 0; | |
| sigaction(SIGINT, &sigint_action, NULL); | |
| sigaction(SIGTERM, &sigint_action, NULL); | |
| auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL { | |
| return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false; | |
| }; | |
| SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true); | |
| cli_context ctx_cli(params); | |
| if (!ctx_cli.init()) { | |
| return 1; | |
| } | |
| return ctx_cli.run(); | |
| } | |