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: 3,687 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 53 54 55 | #pragma once
/* device */
void apir_device_get_device_count(struct virtgpu * gpu);
int apir_device_get_count(struct virtgpu * gpu);
char * apir_device_get_name(struct virtgpu * gpu);
char * apir_device_get_description(struct virtgpu * gpu);
uint32_t apir_device_get_type(struct virtgpu * gpu);
void apir_device_get_memory(struct virtgpu * gpu, size_t * free, size_t * total);
bool apir_device_supports_op(struct virtgpu * gpu, const ggml_tensor * op);
apir_buffer_type_host_handle_t apir_device_get_buffer_type(struct virtgpu * gpu);
void apir_device_get_props(struct virtgpu * gpu,
bool * async,
bool * host_buffer,
bool * buffer_from_host_ptr,
bool * events,
bool * mmap_support);
apir_buffer_context_t apir_device_buffer_from_ptr(struct virtgpu * gpu, size_t size, size_t max_tensor_size);
/* buffer-type */
char * apir_buffer_type_get_name(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle);
size_t apir_buffer_type_get_alignment(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle);
size_t apir_buffer_type_get_max_size(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle);
/* apir_buffer_type_is_host is deprecated. */
apir_buffer_context_t apir_buffer_type_alloc_buffer(struct virtgpu * gpu,
apir_buffer_type_host_handle_t host_handle,
size_t size);
size_t apir_buffer_type_get_alloc_size(struct virtgpu * gpu,
apir_buffer_type_host_handle_t host_handle,
const ggml_tensor * op);
/* buffer */
void * apir_buffer_get_base(struct virtgpu * gpu, apir_buffer_context_t * buffer_context);
void apir_buffer_set_tensor(struct virtgpu * gpu,
apir_buffer_context_t * buffer_context,
ggml_tensor * tensor,
const void * data,
size_t offset,
size_t size);
void apir_buffer_get_tensor(struct virtgpu * gpu,
apir_buffer_context_t * buffer_context,
const ggml_tensor * tensor,
void * data,
size_t offset,
size_t size);
bool apir_buffer_cpy_tensor(struct virtgpu * gpu,
apir_buffer_context_t * buffer_context,
const ggml_tensor * src,
const ggml_tensor * dst);
void apir_buffer_clear(struct virtgpu * gpu, apir_buffer_context_t * buffer_context, uint8_t value);
void apir_buffer_free_buffer(struct virtgpu * gpu, apir_buffer_context_t * buffer_context);
/* backend */
ggml_status apir_backend_graph_compute(struct virtgpu * gpu, ggml_cgraph * cgraph);
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