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 src/llama-mmap.h from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.85 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/src/llama-mmap.h
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/src/llama-mmap.h
-
curl -L -o llama-mmap.h https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/src/llama-mmap.h
1.85 kB
| struct llama_file; | |
| struct llama_mmap; | |
| struct llama_mlock; | |
| using llama_files = std::vector<std::unique_ptr<llama_file>>; | |
| using llama_mmaps = std::vector<std::unique_ptr<llama_mmap>>; | |
| using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>; | |
| struct llama_file { | |
| llama_file(const char * fname, const char * mode, bool use_direct_io = false); | |
| llama_file(FILE * file); | |
| ~llama_file(); | |
| size_t tell() const; | |
| size_t size() const; | |
| int file_id() const; // fileno overload | |
| void seek(size_t offset, int whence) const; | |
| void read_raw(void * ptr, size_t len); | |
| void read_raw_unsafe(void * ptr, size_t len); | |
| void read_aligned_chunk(void * dest, size_t size); | |
| uint32_t read_u32(); | |
| void write_raw(const void * ptr, size_t len) const; | |
| void write_u32(uint32_t val) const; | |
| size_t read_alignment() const; | |
| bool has_direct_io() const; | |
| private: | |
| struct impl; | |
| std::unique_ptr<impl> pimpl; | |
| }; | |
| struct llama_mmap { | |
| // list of [first, last) byte ranges within a file | |
| using ranges = std::vector<std::pair<size_t, size_t>>; | |
| llama_mmap(const llama_mmap &) = delete; | |
| llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1, bool numa = false, | |
| const ranges & lazy_ranges = {}); | |
| ~llama_mmap(); | |
| size_t size() const; | |
| void * addr() const; | |
| void unmap_fragment(size_t first, size_t last); | |
| static const bool SUPPORTED; | |
| private: | |
| struct impl; | |
| std::unique_ptr<impl> pimpl; | |
| }; | |
| struct llama_mlock { | |
| llama_mlock(); | |
| ~llama_mlock(); | |
| void init(void * ptr); | |
| void grow_to(size_t target_size); | |
| static const bool SUPPORTED; | |
| private: | |
| struct impl; | |
| std::unique_ptr<impl> pimpl; | |
| }; | |
| size_t llama_path_max(); | |