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
File size: 1,730 Bytes
90ad830 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | #include "arg.h"
#include "common.h"
#include "log.h"
#include "cli-context.h"
#include <signal.h>
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
# define NOMINMAX
#endif
#include <windows.h>
#endif
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
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);
}
#endif
// 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;
}
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
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);
#elif defined (_WIN32)
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);
#endif
cli_context ctx_cli(params);
if (!ctx_cli.init()) {
return 1;
}
return ctx_cli.run();
}
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