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-virtgpu/virtgpu.h from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 3.19 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ggml/src/ggml-virtgpu/virtgpu.h
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/ggml/src/ggml-virtgpu/virtgpu.h
-
curl -L -o virtgpu.h https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ggml/src/ggml-virtgpu/virtgpu.h
3.19 kB
| // clang-format off | |
| // clang-format on | |
| // Will be defined include/drm/virtgpu_drm.h when | |
| // https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590/diffs | |
| // is merged | |
| // Mesa/Virlgrenderer Venus internal. Only necessary during the | |
| // Venus->APIR transition in Virglrenderer | |
| typedef uint32_t virgl_renderer_capset; | |
| /* from src/virtio/vulkan/vn_renderer_virtgpu.c */ | |
| enum virt_gpu_result_t { | |
| APIR_SUCCESS = 0, | |
| APIR_ERROR_INITIALIZATION_FAILED = -1, | |
| }; | |
| struct virtgpu { | |
| bool use_apir_capset; | |
| int fd; | |
| struct { | |
| virgl_renderer_capset id; | |
| uint32_t version; | |
| virgl_renderer_capset_apir data; | |
| } capset; | |
| util_sparse_array shmem_array; | |
| /* APIR communication pages */ | |
| virtgpu_shmem reply_shmem; | |
| virtgpu_shmem data_shmem; | |
| /* Mutex to protect shared data_shmem buffer from concurrent access */ | |
| mtx_t data_shmem_mutex; | |
| /* Cached device information to prevent memory leaks and race conditions */ | |
| struct { | |
| char * description; | |
| char * name; | |
| int32_t device_count; | |
| uint32_t type; | |
| size_t memory_free; | |
| size_t memory_total; | |
| } cached_device_info; | |
| /* Cached buffer type information to prevent memory leaks and race conditions */ | |
| struct { | |
| apir_buffer_type_host_handle_t host_handle; | |
| char * name; | |
| size_t alignment; | |
| size_t max_size; | |
| } cached_buffer_type; | |
| }; | |
| static inline int virtgpu_ioctl(virtgpu * gpu, unsigned long request, void * args) { | |
| return drmIoctl(gpu->fd, request, args); | |
| } | |
| virtgpu * create_virtgpu(); | |
| apir_encoder * remote_call_prepare(virtgpu * gpu, ApirCommandType apir_cmd_type, int32_t cmd_flags); | |
| uint32_t remote_call(virtgpu * gpu, | |
| apir_encoder * enc, | |
| apir_decoder ** dec, | |
| float max_wait_ms, | |
| long long * call_duration_ns); | |
| void remote_call_finish(virtgpu * gpu, apir_encoder * enc, apir_decoder * dec); | |