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
Download tests/test-peg-parser.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 624 Bytes
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tests/test-peg-parser.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tests/test-peg-parser.cpp
-
curl -L -o test-peg-parser.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tests/test-peg-parser.cpp
624 Bytes
| int main(int argc, char *argv[]) { | |
| testing t(std::cout); | |
| if (argc >= 2) { | |
| t.set_filter(argv[1]); | |
| } | |
| const char * verbose = getenv("LLAMA_TEST_VERBOSE"); | |
| if (verbose) { | |
| t.verbose = std::string(verbose) == "1"; | |
| } | |
| t.test("basic", test_basic); | |
| t.test("unicode", test_unicode); | |
| t.test("json", test_json_parser); | |
| t.test("gbnf", test_gbnf_generation); | |
| t.test("serialization", test_json_serialization); | |
| t.test("python-dict", test_python_dict_parser); | |
| return t.summary(); | |
| } | |