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
File size: 1,097 Bytes
c59fd93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | #include "ggml-et-memops.h"
#include "ggml-et-kernels.h"
#include "ggml-impl.h"
// 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;
}
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