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: 1,529 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 | #pragma once
#include "server-common.h"
#include "server-http.h"
#include "server-queue.h"
#include "server-mcp.h"
#include <atomic>
#include <functional>
#include <memory>
struct server_tool {
std::string name;
std::string display_name;
bool permission_write = false;
bool support_stream = false; // if true, output can be streamed
bool uses_cwd = false; // if true, the tool resolves paths and runs against the working directory
virtual ~server_tool() = default;
virtual json get_definition() const = 0;
virtual std::string type() const { return "server"; }
struct stream {
server_response & qr;
int id;
std::function<bool()> alive;
void push(const std::string & chunk);
};
virtual json invoke(json params, stream * st = nullptr) const = 0;
json to_json() const;
};
struct server_tools_runtime; // impl detail, defined in server-tools.cpp
struct server_tools {
std::vector<std::unique_ptr<server_tool>> tools;
// for streaming
server_response queue_res;
std::atomic<int> res_id{0};
// set when --tools-runtime is configured; routes every tool call through an isolate
std::unique_ptr<server_tools_runtime> runtime;
void setup(const std::vector<std::string> & enabled_tools,
server_mcp & mcp_mgr,
const std::string & tools_runtime);
server_http_context::handler_t handle_get;
server_http_context::handler_t handle_post;
server_tools();
~server_tools();
};
|