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 tools/cli/cli-client.h from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.43 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/cli/cli-client.h
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/cli/cli-client.h
-
curl -L -o cli-client.h https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/cli/cli-client.h
1.43 kB
| // openai-like client for CLI | |
| struct cli_client { | |
| std::string server_base; // base url, for example "http://127.0.0.1:8080" | |
| std::string last_error; // set when wait_health() fails | |
| std::string model; // optional, set when the server has multiple models (router mode) | |
| // simple GET request, returns the raw response body | |
| // throws std::runtime_error on transport error or non-2xx status | |
| std::string get(const std::string & path); | |
| // simple POST request, returns the raw response body | |
| // throws std::runtime_error on transport error or non-2xx status | |
| std::string post(const std::string & path, const std::string & body); | |
| // POST request with an SSE streaming response | |
| // on_data is invoked per "data:" event with the raw event payload | |
| // returns after the stream is finished (empty string on graceful exit) | |
| // otherwise, the raw error response body | |
| std::string post_sse(const std::string & path, | |
| const std::string & body, | |
| const std::function<bool()> & should_stop, | |
| const std::function<void(const std::string &)> & on_data); | |
| // poll /health until the server is ready to accept requests | |
| // returns false if is_aborted returned true or the server is unreachable | |
| bool wait_health(const std::function<bool()> & is_aborted); | |
| }; | |