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
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Download FAST2-FORK.md from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 979 Bytes
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/FAST2-FORK.md
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/FAST2-FORK.md
-
curl -L -o FAST2-FORK.md https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/FAST2-FORK.md
979 Bytes
llama.cpp — K2-FAST2 branch
This repository publishes branch k2-fast2: MBZUAI-IFM's llama.cpp
model/K2Horizon support (base commit 42adf01) plus the FAST2 sliding-window
attention override for k2-horizon GGUFs (51 lines in src/models/k2-horizon.cpp).
- Activates
k2-horizon.attention.sliding_windowGGUF metadata (e.g. the FAST2-SWA8K model) or--override-kv k2-horizon.attention.sliding_window=int:W - Measured with K2-Horizon-MoVA-36B-A4B APEX-Mini @61K ctx on RTX 2080 Ti: decode 16.5 -> 50.7 tok/s (3.1x), prefill 692 -> 1482 tok/s (2.1x), ppl inside window identical to full attention
- Stock behavior is unchanged when no window key is present
Build:
cmake -B build -DGGML_CUDA=ON -DGGML_CUDA_FA_ALL_QUANTS=ON -DLLAMA_CURL=OFF \
-DCMAKE_CUDA_ARCHITECTURES=<your-sm> -DCMAKE_BUILD_TYPE=Release
cmake --build build --target llama-server -j
The same change as a standalone patch: k2-fast2-swa.patch in the FAST2 model repo.