Instructions to use eugenehp/miratts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use eugenehp/miratts with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="eugenehp/miratts", filename="MiraTTS.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/miratts 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/miratts:Q4_K_M # Run inference directly in the terminal: llama cli -hf eugenehp/miratts: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/miratts:Q4_K_M # Run inference directly in the terminal: llama cli -hf eugenehp/miratts: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/miratts:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf eugenehp/miratts: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/miratts:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf eugenehp/miratts:Q4_K_M
Use Docker
docker model run hf.co/eugenehp/miratts:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use eugenehp/miratts with Ollama:
ollama run hf.co/eugenehp/miratts:Q4_K_M
- Unsloth Studio
How to use eugenehp/miratts 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/miratts 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/miratts to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for eugenehp/miratts to start chatting
- Pi
How to use eugenehp/miratts with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf eugenehp/miratts:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "eugenehp/miratts:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use eugenehp/miratts with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf eugenehp/miratts:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default eugenehp/miratts:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use eugenehp/miratts with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf eugenehp/miratts:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "eugenehp/miratts:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use eugenehp/miratts with Docker Model Runner:
docker model run hf.co/eugenehp/miratts:Q4_K_M
- Lemonade
How to use eugenehp/miratts with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull eugenehp/miratts:Q4_K_M
Run and chat with the model
lemonade run user.miratts-Q4_K_M
List all available models
lemonade list
File size: 1,722 Bytes
14301c4 74feb31 14301c4 74feb31 14301c4 74feb31 14301c4 74feb31 14301c4 74feb31 14301c4 74feb31 14301c4 74feb31 | 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 61 62 63 | ---
license: cc-by-nc-sa-4.0
pipeline_tag: text-to-speech
library_name: rlx
tags:
- miratts
- tts
- rlx
---
# MiraTTS (RLX staging)
MiraTTS LM + ONNX decoders for RLX. Non-commercial license — see upstream.
| Field | Value |
|---|---|
| **Hub id** | [`eugenehp/miratts`](https://huggingface.co/eugenehp/miratts) |
| **Kind** | Staging redistrib of an upstream checkpoint for RLX runners. |
| **RLX crate** | [`rlx-miratts`](https://github.com/MIT-RLX/rlx-models/tree/main/crates/rlx-miratts) |
| **Upstream** | https://huggingface.co/YatharthS/MiraTTS |
## Quick start
```bash
hf download eugenehp/miratts --local-dir .
cargo run -p rlx-miratts --release -- --model-dir .
```
## File highlights
- `model.safetensors` (966.4 MiB)
- `MiraTTS.Q4_K_M.gguf` (392.6 MiB)
- `decoders/detokenizer.onnx` (183.8 MiB)
- `decoders/q_encoder.onnx` (116.7 MiB)
- `decoders/processer.onnx` (83.4 MiB)
- `decoders/s_encoder.onnx` (22.7 MiB)
- `tokenizer.json` (13.4 MiB)
- `vocab.json` (2.6 MiB)
- `tokenizer_config.json` (2.4 MiB)
- `merges.txt` (1.6 MiB)
- `added_tokens.json` (460.6 KiB)
- `config.json` (1.3 KiB)
- `special_tokens_map.json` (613 B)
- `generation_config.json` (170 B)
## Run with RLX
Clone [rlx-models](https://github.com/MIT-RLX/rlx-models), place this repo under `weights/tts/miratts` (or pass the path explicitly), then:
```bash
cargo run -p rlx-miratts --release -- --model-dir .
```
## License
CC-BY-NC-SA-4.0 — see upstream license; non-commercial share-alike.
Original weights and authorship: https://huggingface.co/YatharthS/MiraTTS
## 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.
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