Instructions to use second-state/OuteTTS-0.3-1B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use second-state/OuteTTS-0.3-1B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="second-state/OuteTTS-0.3-1B-GGUF",
	filename="OuteTTS-0.3-1B-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "\"The answer to the universe is 42\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use second-state/OuteTTS-0.3-1B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf second-state/OuteTTS-0.3-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/OuteTTS-0.3-1B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf second-state/OuteTTS-0.3-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/OuteTTS-0.3-1B-GGUF:Q4_K_M

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 second-state/OuteTTS-0.3-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/OuteTTS-0.3-1B-GGUF:Q4_K_M

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 second-state/OuteTTS-0.3-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/OuteTTS-0.3-1B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/OuteTTS-0.3-1B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use second-state/OuteTTS-0.3-1B-GGUF with Ollama:
```
ollama run hf.co/second-state/OuteTTS-0.3-1B-GGUF:Q4_K_M
```

Unsloth Studio

How to use second-state/OuteTTS-0.3-1B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for second-state/OuteTTS-0.3-1B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for second-state/OuteTTS-0.3-1B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for second-state/OuteTTS-0.3-1B-GGUF to start chatting

Docker Model Runner
How to use second-state/OuteTTS-0.3-1B-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/OuteTTS-0.3-1B-GGUF:Q4_K_M
```

Lemonade

How to use second-state/OuteTTS-0.3-1B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/OuteTTS-0.3-1B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OuteTTS-0.3-1B-GGUF-Q4_K_M

List all available models

lemonade list

OuteTTS-0.3-1B-GGUF

Original Model

OuteAI/OuteTTS-0.3-1B

Run with LlamaEdge

LlamaEdge version: coming soon

Quantized GGUF Models

Name	Quant method	Bits	Size	Use case
OuteTTS-0.3-1B-Q2_K.gguf	Q2_K	2	491 MB	smallest, significant quality loss - not recommended for most purposes
OuteTTS-0.3-1B-Q3_K_L.gguf	Q3_K_L	3	663 MB	small, substantial quality loss
OuteTTS-0.3-1B-Q3_K_M.gguf	Q3_K_M	3	616 MB	very small, high quality loss
OuteTTS-0.3-1B-Q3_K_S.gguf	Q3_K_S	3	560 MB	very small, high quality loss
OuteTTS-0.3-1B-Q4_0.gguf	Q4_0	4	702 MB	legacy; small, very high quality loss - prefer using Q3_K_M
OuteTTS-0.3-1B-Q4_K_M.gguf	Q4_K_M	4	745 MB	medium, balanced quality - recommended
OuteTTS-0.3-1B-Q4_K_S.gguf	Q4_K_S	4	709 MB	small, greater quality loss
OuteTTS-0.3-1B-Q5_0.gguf	Q5_0	5	836 MB	legacy; medium, balanced quality - prefer using Q4_K_M
OuteTTS-0.3-1B-Q5_K_M.gguf	Q5_K_M	5	859 MB	large, very low quality loss - recommended
OuteTTS-0.3-1B-Q5_K_S.gguf	Q5_K_S	5	836 MB	large, low quality loss - recommended
OuteTTS-0.3-1B-Q6_K.gguf	Q6_K	6	979 MB	very large, extremely low quality loss
OuteTTS-0.3-1B-Q8_0.gguf	Q8_0	8	1.27 GB	very large, extremely low quality loss - not recommended
OuteTTS-0.3-1B-f16.gguf	f16	16	2.38 GB
wavtokenizer-large-75-ggml-f16.gguf	f16	16	1.00 GB

Quantized with llama.cpp b4497

Downloads last month: 331

GGUF

Model size

1B params

Architecture

olmo

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for second-state/OuteTTS-0.3-1B-GGUF

Base model

OuteAI/OuteTTS-0.3-1B

Quantized

(4)

this model