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-rset-release.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.79 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tests/test-rset-release.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tests/test-rset-release.cpp
-
curl -L -o test-rset-release.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tests/test-rset-release.cpp
1.79 kB
| // ref: https://github.com/ggml-org/llama.cpp/issues/25937 | |
| // only works reliably when run with a large model that occupies 3GB+ of wired memory | |
| // thus, this test is not run by default | |
| // example model to run with: google/gemma-4-E4B-it-qat-q4_0-gguf | |
| static uint64_t wired_memory() { | |
| vm_statistics64_data_t vmstat; | |
| mach_msg_type_number_t count = HOST_VM_INFO64_COUNT; | |
| if (host_statistics64(mach_host_self(), HOST_VM_INFO64, (host_info64_t)&vmstat, &count) != KERN_SUCCESS) { | |
| return UINT64_MAX; | |
| } | |
| return static_cast<uint64_t>(vmstat.wire_count) * vm_kernel_page_size; | |
| } | |
| int main(int argc, char ** argv) { | |
| auto * model_path = common_get_model_or_exit(argc, argv); | |
| llama_backend_init(); | |
| const uint64_t wired_initial = wired_memory(); | |
| llama_model_params params = llama_model_default_params(); | |
| params.load_mode = LLAMA_LOAD_MODE_NONE; | |
| struct llama_model* model = llama_model_load_from_file(model_path, params); | |
| const uint64_t wired_loaded = wired_memory(); | |
| const uint64_t wired_delta = wired_loaded - wired_initial; | |
| // system memory fluctuates, so we need to allocate enough to reliably detect the release | |
| GGML_ASSERT(wired_delta > 2'000'000'000); // 2GB | |
| llama_model_free(model); | |
| const uint64_t t_start_ms = ggml_time_ms(); | |
| // expect most of the allocated memory to be released within 10 seconds | |
| // we allow for some tolerance due to system-wide memory fluctuations | |
| while (wired_memory() > wired_loaded - 0.75 * wired_delta) { | |
| GGML_ASSERT(ggml_time_ms() - t_start_ms < 10'000); | |
| usleep(100'000); // 100ms | |
| } | |
| llama_backend_free(); | |
| return 0; | |
| } | |