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: 1,569 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 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | #include "backend-dispatched.h"
#include "backend-virgl-apir.h"
#include "ggml-backend-impl.h"
#include "ggml-backend.h"
#include "ggml-impl.h"
#include <cstdint>
ggml_backend_reg_t reg = NULL;
ggml_backend_dev_t dev = NULL;
ggml_backend_t bck = NULL;
uint64_t timer_start = 0;
uint64_t timer_total = 0;
uint64_t timer_count = 0;
uint32_t backend_dispatch_initialize(void * ggml_backend_reg_fct_p) {
if (reg != NULL) {
GGML_LOG_WARN(GGML_VIRTGPU_BCK "%s: already initialized\n", __func__);
return APIR_BACKEND_INITIALIZE_ALREADY_INITED;
}
ggml_backend_reg_t (*ggml_backend_reg_fct)(void) = (ggml_backend_reg_t (*)()) ggml_backend_reg_fct_p;
reg = ggml_backend_reg_fct();
if (reg == NULL) {
GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: backend registration failed\n", __func__);
return APIR_BACKEND_INITIALIZE_BACKEND_REG_FAILED;
}
size_t device_count = reg->iface.get_device_count(reg);
if (!device_count) {
GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: no device found\n", __func__);
return APIR_BACKEND_INITIALIZE_NO_DEVICE;
}
dev = reg->iface.get_device(reg, 0);
if (!dev) {
GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: failed to get device\n", __func__);
return APIR_BACKEND_INITIALIZE_NO_DEVICE;
}
bck = dev->iface.init_backend(dev, NULL);
if (!bck) {
GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: backend initialization failed\n", __func__);
return APIR_BACKEND_INITIALIZE_BACKEND_INIT_FAILED;
}
return APIR_BACKEND_INITIALIZE_SUCCESS;
}
|