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 app/download.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 2.21 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/app/download.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/app/download.cpp
-
curl -L -o download.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/app/download.cpp
2.21 kB
| static void print_usage(int /*argc*/, char ** argv) { | |
| printf( | |
| "\nexamples:\n" | |
| " %s -hf ggml-org/gemma-3-4b-it-qat-GGUF\n" | |
| " %s -hf ggml-org/gemma-3-4b-it-qat-GGUF:Q4_K_M\n" | |
| " %s -hf ggml-org/models -hff model.gguf\n" | |
| " %s -mu https://example.com/model.gguf -m model.gguf\n" | |
| "\n", | |
| argv[0], argv[0], argv[0], argv[0] | |
| ); | |
| } | |
| int llama_download(int argc, char ** argv); | |
| int llama_download(int argc, char ** argv) { | |
| common_init(); | |
| common_params params; | |
| params.verbosity = LOG_LEVEL_ERROR; | |
| if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_DOWNLOAD, print_usage)) { | |
| return 1; | |
| } | |
| const bool has_source = !params.model.hf_repo.empty() || !params.model.url.empty() || | |
| !params.model.path.empty() || !params.model.docker_repo.empty(); | |
| if (!has_source) { | |
| fprintf(stderr, "error: no model source specified (use --hf-repo, --model-url, --model or --docker-repo)\n"); | |
| return 1; | |
| } | |
| try { | |
| common_models_handler handler = common_models_handler_init(params, LLAMA_EXAMPLE_DOWNLOAD); | |
| common_models_handler_apply(handler, params); | |
| } catch (const std::exception & e) { | |
| fprintf(stderr, "error: %s\n", e.what()); | |
| return 1; | |
| } | |
| if (!params.models_preset.empty()) { | |
| // -hf pointed at a preset repo: print the preset path and stop | |
| printf("%s\n", params.models_preset.c_str()); | |
| return 0; | |
| } | |
| if (params.model.path.empty()) { | |
| fprintf(stderr, "error: model download failed\n"); | |
| return 1; | |
| } | |
| if (!std::filesystem::exists(params.model.path)) { | |
| fprintf(stderr, "error: model file does not exist: %s\n", params.model.path.c_str()); | |
| return 1; | |
| } | |
| printf("%s\n", params.model.path.c_str()); | |
| if (!params.mmproj.path.empty()) { | |
| printf("%s\n", params.mmproj.path.c_str()); | |
| } | |
| if (!params.speculative.draft.mparams.path.empty()) { | |
| printf("%s\n", params.speculative.draft.mparams.path.c_str()); | |
| } | |
| return 0; | |
| } | |