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 ggml/src/ggml-backend-dl.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.05 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ggml/src/ggml-backend-dl.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/ggml/src/ggml-backend-dl.cpp
-
curl -L -o ggml-backend-dl.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ggml/src/ggml-backend-dl.cpp
1.05 kB
| dl_handle * dl_load_library(const fs::path & path) { | |
| // suppress error dialogs for missing DLLs | |
| DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS); | |
| SetErrorMode(old_mode | SEM_FAILCRITICALERRORS); | |
| HMODULE handle = LoadLibraryW(path.wstring().c_str()); | |
| SetErrorMode(old_mode); | |
| return handle; | |
| } | |
| void * dl_get_sym(dl_handle * handle, const char * name) { | |
| DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS); | |
| SetErrorMode(old_mode | SEM_FAILCRITICALERRORS); | |
| void * p = (void *) GetProcAddress(handle, name); | |
| SetErrorMode(old_mode); | |
| return p; | |
| } | |
| const char * dl_error() { | |
| return ""; | |
| } | |
| dl_handle * dl_load_library(const fs::path & path) { | |
| dl_handle * handle = dlopen(path.string().c_str(), RTLD_NOW | RTLD_LOCAL); | |
| return handle; | |
| } | |
| void * dl_get_sym(dl_handle * handle, const char * name) { | |
| return dlsym(handle, name); | |
| } | |
| const char * dl_error() { | |
| const char *rslt = dlerror(); | |
| return rslt != nullptr ? rslt : ""; | |
| } | |