Instructions to use KittenML/kitten-tts-2 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 KittenML/kitten-tts-2 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 KittenML/kitten-tts-2 # Run inference directly in the terminal: llama cli -hf KittenML/kitten-tts-2
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf KittenML/kitten-tts-2 # Run inference directly in the terminal: llama cli -hf KittenML/kitten-tts-2
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf KittenML/kitten-tts-2 # Run inference directly in the terminal: ./llama-cli -hf KittenML/kitten-tts-2
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf KittenML/kitten-tts-2 # Run inference directly in the terminal: ./build/bin/llama-cli -hf KittenML/kitten-tts-2
Use Docker
docker model run hf.co/KittenML/kitten-tts-2
- LM Studio
- Jan
- Ollama
How to use KittenML/kitten-tts-2 with Ollama:
ollama run hf.co/KittenML/kitten-tts-2
- Unsloth Desktop
- Docker Model Runner
How to use KittenML/kitten-tts-2 with Docker Model Runner:
docker model run hf.co/KittenML/kitten-tts-2
- Lemonade
How to use KittenML/kitten-tts-2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull KittenML/kitten-tts-2
Run and chat with the model
lemonade run user.kitten-tts-2-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Describe the llama.cpp bundle in config.json
Browse files- config.json +41 -2
config.json
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},
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"lm_packed": "lm/model-ternary.safetensors",
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"lm_emb4": "lm/model-tl2-emb4.safetensors",
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"default_weights": "packed"
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},
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"lm_packed": "lm/model-ternary.safetensors",
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"lm_emb4": "lm/model-tl2-emb4.safetensors",
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"default_weights": "packed",
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"cpp": {
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"version": 1,
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"gguf": {
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"file": "cpp/model-tq2_1.gguf",
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"size": 1029076832
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"decoders": {
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"default": {
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"torchscript": {
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"file": "cpp/default/decoder.pt",
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"size": 534310697
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"voices": {
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"file": "cpp/default/voices.json",
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"size": 32744538
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"student_w4": {
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"size": 312339253
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"voices": {
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"file": "cpp/student_w4/voices.json",
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"size": 13531605
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"student_w8": {
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"torchscript": {
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"file": "cpp/student_w8/decoder.pt",
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"size": 312339253
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"voices": {
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