Instructions to use VladHong/llama-cpp-K2-FAST2 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 VladHong/llama-cpp-K2-FAST2 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 VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: llama cli -hf VladHong/llama-cpp-K2-FAST2
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: llama cli -hf VladHong/llama-cpp-K2-FAST2
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: ./build/bin/llama cli -hf VladHong/llama-cpp-K2-FAST2
Use Docker
docker model run hf.co/VladHong/llama-cpp-K2-FAST2
- Llama new
- LM Studio
- Jan
- Ollama
How to use VladHong/llama-cpp-K2-FAST2 with Ollama:
ollama run hf.co/VladHong/llama-cpp-K2-FAST2
- Unsloth Desktop
- Docker Model Runner
How to use VladHong/llama-cpp-K2-FAST2 with Docker Model Runner:
docker model run hf.co/VladHong/llama-cpp-K2-FAST2
- Lemonade
How to use VladHong/llama-cpp-K2-FAST2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull VladHong/llama-cpp-K2-FAST2
Run and chat with the model
lemonade run user.llama-cpp-K2-FAST2-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 1,305 Bytes
c59fd93 | 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 | #include "log.h"
#include <cstdlib>
#include <thread>
int main() {
const int n_thread = 8;
std::thread threads[n_thread];
for (int i = 0; i < n_thread; i++) {
threads[i] = std::thread([i]() {
const int n_msg = 1000;
for (int j = 0; j < n_msg; j++) {
const int log_type = std::rand() % 4;
switch (log_type) {
case 0: LOG_INF("Thread %d: %d\n", i, j); break;
case 1: LOG_WRN("Thread %d: %d\n", i, j); break;
case 2: LOG_ERR("Thread %d: %d\n", i, j); break;
case 3: LOG_DBG("Thread %d: %d\n", i, j); break;
default:
break;
}
if (rand () % 10 < 5) {
common_log_set_timestamps(common_log_main(), rand() % 2);
common_log_set_prefix (common_log_main(), rand() % 2);
}
}
});
}
for (int i = 0; i < n_thread; i++) {
threads[i].join();
}
common_log_flush(common_log_main());
// We explicitly free the logger singleton to avoid hanging on Windows
// related to timing issues of thread startup and DLL teardown
common_log_free(common_log_main());
return 0;
}
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