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

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

llama-cpp-K2-FAST2

File size: 81 Bytes

c59fd93

declare module 'virtual:nerdamer' {
	const code: string;
	export default code;
}