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 ci/README.md from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.3 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ci/README.md
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/ci/README.md
-
curl -L -o README.md https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ci/README.md
1.3 kB
| # CI | |
| This CI implements heavy-duty workflows that run on self-hosted runners. Typically the purpose of these workflows is to | |
| cover hardware configurations that are not available from Github-hosted runners and/or require more computational | |
| resource than normally available. | |
| It is a good practice, before publishing changes to execute the full CI locally on your machine. For example: | |
| ```bash | |
| mkdir tmp | |
| # CPU-only build | |
| bash ./ci/run.sh ./tmp/results ./tmp/mnt | |
| # with CUDA support | |
| GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt | |
| # with SYCL support | |
| source /opt/intel/oneapi/setvars.sh | |
| GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt | |
| # with MUSA support | |
| GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt | |
| # etc. | |
| ``` | |
| # Adding self-hosted runners | |
| - Add a self-hosted `ggml-ci` workflow to [[.github/workflows/build.yml]] with an appropriate label | |
| - Request a runner token from `ggml-org` (for example, via a comment in the PR or email) | |
| - Set-up a machine using the received token ([docs](https://docs.github.com/en/actions/how-tos/manage-runners/self-hosted-runners/add-runners)) | |
| - Optionally update [ci/run.sh](https://github.com/ggml-org/llama.cpp/blob/master/ci/run.sh) to build and run on the target platform by gating the implementation with a `GG_BUILD_...` env | |