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: 2,476 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 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | #pragma once
#include <thread>
#include "http.h"
// llama_server will be available as a dynamic library symbol
int llama_server(common_params & params, int argc, char ** argv);
void llama_server_terminate();
struct cli_server {
std::thread th;
int port = -1;
std::atomic<bool> is_alive = false;
std::atomic<bool> is_stopping = false;
~cli_server() {
stop();
}
void stop() {
if (is_stopping.exchange(true)) {
return;
}
if (alive()) {
llama_server_terminate();
}
if (th.joinable()) {
th.join();
}
}
// spawn llama-server in a thread and interact with it via a random port
bool start(common_params & params) {
port = common_http_get_free_port();
if (port <= 0) {
fprintf(stderr, "failed to get a free port\n");
exit(1);
}
is_alive.store(true, std::memory_order_release);
common_params server_params = params; // copy
server_params.port = port;
th = std::thread([this, server_params]() mutable {
// argc / argv are only used in router mode, we can skip them for now
int res = llama_server(server_params, 0, nullptr);
if (res != 0) {
fprintf(stderr, "llama_server exited with code %d\n", res);
}
is_alive.store(false, std::memory_order_release);
});
return true;
}
std::string address() const {
return "http://127.0.0.1:" + std::to_string(port);
}
bool wait_ready(std::function<bool()> should_stop) {
if (!alive()) {
return false;
}
while (!should_stop()) {
auto [cli, parts] = common_http_client(address());
cli.set_connection_timeout(1, 0);
auto res = cli.Get("/health");
if (res) {
if (res->status == 200) {
return true;
}
// any other status means the server is up but not ready yet
// (e.g. 503 while the model is still loading)
}
if (!alive()) {
// in case server die permanently
return false;
}
std::this_thread::sleep_for(std::chrono::milliseconds(200));
}
return true;
}
bool alive() const {
return is_alive.load(std::memory_order_acquire);
}
};
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