Instructions to use eugenehp/miotts 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 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
File size: 1,622 Bytes
47b815f 7021eb2 47b815f 7021eb2 47b815f 7021eb2 47b815f 7021eb2 47b815f 7021eb2 47b815f 7021eb2 47b815f 7021eb2 47b815f 7021eb2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | ---
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.
|