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/tts/README.md from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 2 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/tts/README.md
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/tts/README.md
-
curl -L -o README.md https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/tts/README.md
2 kB
llama.cpp TTS
This is a tool to demonstrate audio generation capability in llama.cpp via libmtmd. It was added via PR #26254
Note: this tool used to serve as a demo for OuteTTS, but it was converted to a more model-agnostic tool.
Common usage
Simple usage:
llama-tts -hf ggml-org/Qwen3-TTS-12Hz-1.7B-Base-GGUF -p "Hello world" --output out.wav
Common params:
- Sampling params such as
--top-k,--top-p,--temp, etc. -n <number_of_frames>limits the output length, e.g.-n 500. Note that how many milliseconds each frame represents varies by model- Core inference params such as
-ngl,-b,-ub, etc.
Qwen3-TTS
Available params:
--tts-langcan bezh,en,de,it,pt,es,ja,ko,fr,ru(default:en)--tts-speaker-fileshould point to a speaker reference audio file (wav, mp3)
Example usage:
llama-tts -hf ggml-org/Qwen3-TTS-12Hz-1.7B-Base-GGUF \
-p "Hello world" \
--tts-lang english \
--tts-speaker-file speaker.mp3 \
--output out.wav
Pocket TTS
Available params:
--tts-speaker-fileshould point to a speaker reference audio file (wav, mp3). It is required, the model produces almost no audio without it- Note:
langis not used, the language is a property of the weights
Example usage:
llama-tts -m pocket-tts.gguf \
-mm mmproj-pocket-tts.gguf \
-p "Hello world" \
--tts-speaker-file speaker.mp3 \
--output out.wav
Note for GGUF conversion:
The upstream repository holds one complete model per language under languages/, next to a set of shared files at the root. Convert one of the languages/<name> directories, not the root directory:
python convert_hf_to_gguf.py path/to/pocket-tts/languages/english --outfile pocket-tts.gguf
python convert_hf_to_gguf.py path/to/pocket-tts/languages/english --mmproj --outfile mmproj-pocket-tts.gguf