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: 1,964 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 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | llama_add_compile_flags()
#
# libraries
#
# llama
file(GLOB LLAMA_MODELS_SOURCES "models/*.cpp")
add_library(llama
../include/llama.h
llama.cpp
llama-adapter.cpp
llama-arch.cpp
llama-batch.cpp
llama-chat.cpp
llama-context.cpp
llama-cparams.cpp
llama-grammar.cpp
llama-graph.cpp
llama-hparams.cpp
llama-impl.cpp
llama-io.cpp
llama-kv-cache.cpp
llama-kv-cache-iswa.cpp
llama-kv-cache-dsa.cpp
llama-kv-cache-dsa-iswa.cpp
llama-kv-cache-msa.cpp
llama-kv-cache-dsv4.cpp
llama-memory.cpp
llama-memory-hybrid.cpp
llama-memory-hybrid-iswa.cpp
llama-memory-hybrid-idx.cpp
llama-memory-recurrent.cpp
llama-mmap.cpp
llama-model-loader.cpp
llama-model-saver.cpp
llama-model.cpp
llama-quant.cpp
llama-sampler.cpp
llama-vocab.cpp
unicode-data.cpp
unicode.cpp
unicode.h
${LLAMA_MODELS_SOURCES}
)
set_target_properties(llama PROPERTIES
VERSION ${LLAMA_VERSION_BASE}
SOVERSION ${LLAMA_VERSION_MAJOR}
MACHO_CURRENT_VERSION 0 # keep macOS linker from seeing oversized version number
)
target_compile_definitions(llama PRIVATE
LLAMA_VERSION="${LLAMA_VERSION}"
LLAMA_COMMIT="${LLAMA_BUILD_COMMIT}"
)
target_include_directories(llama PRIVATE .)
target_include_directories(llama PUBLIC ../include)
target_compile_features (llama PRIVATE cxx_std_17) # don't bump
target_link_libraries(llama PUBLIC ggml)
if (BUILD_SHARED_LIBS)
set_target_properties(llama PROPERTIES POSITION_INDEPENDENT_CODE ON)
target_compile_definitions(llama PRIVATE LLAMA_BUILD)
target_compile_definitions(llama PUBLIC LLAMA_SHARED)
endif()
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