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-et/ggml-et-memops.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.1 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ggml/src/ggml-et/ggml-et-memops.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/ggml/src/ggml-et/ggml-et-memops.cpp
-
curl -L -o ggml-et-memops.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ggml/src/ggml-et/ggml-et-memops.cpp
1.1 kB
| // Kernel parameter structure for memset operation | |
| struct memset_params { | |
| uint32_t op_type; // GGML_ET_MEMOP_MEMSET | |
| uint32_t value; // Value to set (extended to uint32_t for alignment) | |
| void * dst_ptr; // Destination device pointer | |
| size_t size; // Number of bytes to set | |
| }; | |
| bool ggml_et_memset(ggml_backend_et_device_context * dev_ctx, void * dst_ptr, uint8_t value, size_t size) { | |
| if (!dev_ctx || !dst_ptr || size == 0) { | |
| GGML_LOG_ERROR("ET: Invalid memset parameters\n"); | |
| return false; | |
| } | |
| // Prepare kernel parameters | |
| memset_params params; | |
| params.op_type = GGML_ET_MEMOP_MEMSET; | |
| params.value = value; | |
| params.dst_ptr = dst_ptr; | |
| params.size = size; | |
| // Launch memops kernel (will lazy-load if not already loaded) | |
| bool success = ggml_et_launch_kernel(dev_ctx, "memops", ¶ms, sizeof(params)); | |
| if (!success) { | |
| GGML_LOG_ERROR("ET: memset kernel launch failed\n"); | |
| return false; | |
| } | |
| return true; | |
| } | |