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 common/console.h from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.17 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/common/console.h
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/common/console.h
-
curl -L -o console.h https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/common/console.h
1.17 kB
| // Console functions | |
| enum display_type { | |
| DISPLAY_TYPE_RESET = 0, | |
| DISPLAY_TYPE_INFO, | |
| DISPLAY_TYPE_PROMPT, | |
| DISPLAY_TYPE_REASONING, | |
| DISPLAY_TYPE_USER_INPUT, | |
| DISPLAY_TYPE_ERROR | |
| }; | |
| namespace console { | |
| void init(bool use_simple_io, bool use_advanced_display); | |
| void cleanup(); | |
| void set_display(display_type display); | |
| bool readline(std::string & line, bool multiline_input); | |
| using completion_callback = std::function<std::vector<std::pair<std::string, size_t>>(std::string_view, size_t)>; | |
| void set_completion_callback(completion_callback cb); | |
| namespace spinner { | |
| void start(); | |
| void stop(); | |
| } | |
| // note: the logging API below output directly to stdout | |
| // it can negatively impact performance if used on inference thread | |
| // only use in in a dedicated CLI thread | |
| // for logging in inference thread, use log.h instead | |
| LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2) | |
| void log(const char * fmt, ...); | |
| LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2) | |
| void error(const char * fmt, ...); | |
| void flush(); | |
| } | |