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: 1,459 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 | ## cmake-test
This is just for manually testing/developing of a llama.cpp installation to
enable troubleshooting issues and exploration. The idea is that this can be used
after making changes to llama.cpp installation cmake configuration and then
verify it locally.
### Usage
The following will configure, build, and install llama.cpp
Configuring/build/install:
```console
./build-install.sh
```
The above command will create a directory named `install` in the current directory
which will have the follwing files in its lib directory:
```console
(venv) $ ls install/lib/
cmake libggml.so libllama-common.so.0 libllama.so.0.1.0 llama.cpp
libggml-base.so libggml.so.0 libllama-common.so.0.1.0 libmtmd.so pkgconfig
libggml-base.so.0 libggml.so.0.19.0 libllama.so libmtmd.so.0
libggml-base.so.0.19.0 libllama-common.so libllama.so.0 libmtmd.so.0.1.0
```
Build/run this project using the installation created above:
```console
(venv) $ ./build.sh
-- Configuring done (0.0s)
-- Generating done (0.0s)
-- Build files have been written to: /path/to/llama.cpp/examples/test-cmake/build
[100%] Built target test-cmake
[test-cmake] Using llama.cpp version 0.1.0-dev-b10335
[test-cmake] Initializing backend...
load_backend: loaded CPU backend from /path/to/llama.cpp/examples/test-cmake/install/lib/llama.cpp/libggml-cpu-alderlake.so
[test-cmake] Backend initialized.
```
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