Instructions to use eugenehp/miotts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eugenehp/miotts with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="eugenehp/miotts",
	filename="MioTTS-0.6B-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use eugenehp/miotts 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 eugenehp/miotts:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf eugenehp/miotts:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf eugenehp/miotts:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf eugenehp/miotts: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 eugenehp/miotts:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf eugenehp/miotts: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 eugenehp/miotts:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf eugenehp/miotts:Q4_K_M

Use Docker

docker model run hf.co/eugenehp/miotts:Q4_K_M

LM Studio
Jan
Ollama
How to use eugenehp/miotts with Ollama:
```
ollama run hf.co/eugenehp/miotts:Q4_K_M
```

Unsloth Studio

How to use eugenehp/miotts 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 eugenehp/miotts 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 eugenehp/miotts to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for eugenehp/miotts to start chatting

Atomic Chat new
Docker Model Runner
How to use eugenehp/miotts with Docker Model Runner:
```
docker model run hf.co/eugenehp/miotts:Q4_K_M
```

Lemonade

How to use eugenehp/miotts with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull eugenehp/miotts:Q4_K_M

Run and chat with the model

lemonade run user.miotts-Q4_K_M

List all available models

lemonade list

miotts / README.md

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	---
	license: apache-2.0
	pipeline_tag: text-to-speech
	library_name: rlx
	tags:
	- miotts
	- tts
	- rlx
	---

	# MioTTS-0.6B (RLX staging)

	MioTTS-0.6B speech LM + presets/samples for RLX.

	\| Field \| Value \|
	\|---\|---\|
	\| Hub id \| [`eugenehp/miotts`](https://huggingface.co/eugenehp/miotts) \|
	\| Kind \| Staging redistrib of an upstream checkpoint for RLX runners. \|
	\| RLX crate \| [`rlx-miotts`](https://github.com/MIT-RLX/rlx-models/tree/main/crates/rlx-miotts) \|
	\| Upstream \| https://huggingface.co/Aratako/MioTTS-0.6B \|

	## Quick start

	```bash
	hf download eugenehp/miotts --local-dir .
	cargo run -p rlx-miotts --release -- --model-dir . --codec-dir ../miocodec
	```

	## File highlights

	- `model.safetensors` (1.1 GiB)
	- `MioTTS-0.6B-Q4_K_M.gguf` (388.6 MiB)
	- `tokenizer.json` (13.2 MiB)
	- `vocab.json` (2.6 MiB)
	- `tokenizer_config.json` (2.2 MiB)
	- `merges.txt` (1.6 MiB)
	- `added_tokens.json` (302.3 KiB)
	- `config.json` (1.3 KiB)
	- `special_tokens_map.json` (613 B)
	- `chat_template.jinja` (205 B)
	- `generation_config.json` (160 B)

	## Run with RLX

	Clone [rlx-models](https://github.com/MIT-RLX/rlx-models), place this repo under `weights/tts/miotts` (or pass the path explicitly), then:

	```bash
	cargo run -p rlx-miotts --release -- --model-dir . --codec-dir ../miocodec
	```

	## License

	Apache License 2.0 — see `LICENSE`. Inherit upstream terms when redistributing.

	Original weights and authorship: https://huggingface.co/Aratako/MioTTS-0.6B

	## Redistrib note

	This Hub repo exists so RLX recipes have a stable fetch target. When you only need the upstream checkpoint, prefer the Upstream link above.