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/ggml-backend.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 2.6 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ggml/src/ggml-virtgpu/ggml-backend.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/ggml/src/ggml-virtgpu/ggml-backend.cpp
-
curl -L -o ggml-backend.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ggml/src/ggml-virtgpu/ggml-backend.cpp
2.6 kB
| static const char * ggml_backend_remoting_get_name(ggml_backend_t backend) { | |
| UNUSED(backend); | |
| return "API Remoting backend"; | |
| } | |
| static void ggml_backend_remoting_free(ggml_backend_t backend) { | |
| delete backend; | |
| } | |
| static ggml_status ggml_backend_remoting_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { | |
| virtgpu * gpu = DEV_TO_GPU(backend->device); | |
| return apir_backend_graph_compute(gpu, cgraph); | |
| } | |
| static void ggml_backend_remoting_graph_optimize(ggml_backend_t backend, ggml_cgraph * cgraph) { | |
| virtgpu * gpu = DEV_TO_GPU(backend->device); | |
| UNUSED(gpu); | |
| UNUSED(cgraph); | |
| // not working yet | |
| apir_backend_graph_optimize(gpu, cgraph); | |
| } | |
| static ggml_backend_i ggml_backend_remoting_interface = { | |
| /* .get_name = */ ggml_backend_remoting_get_name, | |
| /* .free = */ ggml_backend_remoting_free, | |
| /* .set_tensor_async = */ NULL, // ggml_backend_remoting_set_tensor_async, | |
| /* .get_tensor_async = */ NULL, // ggml_backend_remoting_get_tensor_async, | |
| /* .set_tensor_2d_async = */ NULL, | |
| /* .get_tensor_2d_async = */ NULL, | |
| /* .cpy_tensor_async = */ NULL, // ggml_backend_remoting_cpy_tensor_async, | |
| /* .synchronize = */ NULL, // ggml_backend_remoting_synchronize, | |
| /* .graph_plan_create = */ NULL, | |
| /* .graph_plan_free = */ NULL, | |
| /* .graph_plan_update = */ NULL, | |
| /* .graph_plan_compute = */ NULL, | |
| /* .graph_compute = */ ggml_backend_remoting_graph_compute, | |
| /* .event_record = */ NULL, | |
| /* .event_wait = */ NULL, | |
| /* .graph_optimize = */ ggml_backend_remoting_graph_optimize, | |
| }; | |
| static ggml_guid_t ggml_backend_remoting_guid() { | |
| static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x14, 0x03, 0x86, 0x02, | |
| 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b }; | |
| return &guid; | |
| } | |
| ggml_backend_t ggml_backend_remoting_device_init(ggml_backend_dev_t dev, const char * params) { | |
| UNUSED(params); | |
| ggml_backend_remoting_device_context * ctx = (ggml_backend_remoting_device_context *) dev->context; | |
| ggml_backend_t remoting_backend = new ggml_backend{ | |
| /* .guid = */ ggml_backend_remoting_guid(), | |
| /* .interface = */ ggml_backend_remoting_interface, | |
| /* .device = */ ggml_backend_reg_dev_get(ggml_backend_virtgpu_reg(), ctx->device), | |
| /* .context = */ ctx, | |
| }; | |
| return remoting_backend; | |
| } | |