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/mtmd/models/gemma4ua.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 485 Bytes
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/mtmd/models/gemma4ua.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/mtmd/models/gemma4ua.cpp
-
curl -L -o gemma4ua.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/mtmd/models/gemma4ua.cpp
485 Bytes
| ggml_cgraph * clip_graph_gemma4ua::build() { | |
| ggml_tensor * inp = build_inp_raw(1); | |
| auto cur = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3)); | |
| // Gemma4UnifiedMultimodalEmbedder | |
| { | |
| // embedding_pre_projection_norm | |
| cur = ggml_rms_norm(ctx0, cur, hparams.eps); | |
| cur = build_mm(model.mm_input_proj_w, cur); | |
| cb(cur, "projected", -1); | |
| } | |
| ggml_build_forward_expand(gf, cur); | |
| return gf; | |
| } | |