Instructions to use eugenehp/maya1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use eugenehp/maya1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="eugenehp/maya1", filename="maya1.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/maya1 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/maya1:Q4_K_M # Run inference directly in the terminal: llama cli -hf eugenehp/maya1: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/maya1:Q4_K_M # Run inference directly in the terminal: llama cli -hf eugenehp/maya1: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/maya1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf eugenehp/maya1: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/maya1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf eugenehp/maya1:Q4_K_M
Use Docker
docker model run hf.co/eugenehp/maya1:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use eugenehp/maya1 with Ollama:
ollama run hf.co/eugenehp/maya1:Q4_K_M
- Unsloth Studio
How to use eugenehp/maya1 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/maya1 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/maya1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for eugenehp/maya1 to start chatting
- Pi
How to use eugenehp/maya1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf eugenehp/maya1: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/maya1:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use eugenehp/maya1 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/maya1: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/maya1:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use eugenehp/maya1 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf eugenehp/maya1: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/maya1: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/maya1 with Docker Model Runner:
docker model run hf.co/eugenehp/maya1:Q4_K_M
- Lemonade
How to use eugenehp/maya1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull eugenehp/maya1:Q4_K_M
Run and chat with the model
lemonade run user.maya1-Q4_K_M
List all available models
lemonade list
| license: apache-2.0 | |
| pipeline_tag: text-to-speech | |
| library_name: rlx | |
| tags: | |
| - maya1 | |
| - gguf | |
| - tts | |
| - rlx | |
| # Maya1 Q4_K_M GGUF (RLX staging) | |
| Maya1 speech LM GGUF + tokenizer for RLX (needs SNAC separately). | |
| | Field | Value | | |
| |---|---| | |
| | **Hub id** | [`eugenehp/maya1`](https://huggingface.co/eugenehp/maya1) | | |
| | **Kind** | Staging redistrib of an upstream checkpoint for RLX runners. | | |
| | **RLX crate** | [`rlx-maya1`](https://github.com/MIT-RLX/rlx-models/tree/main/crates/rlx-maya1) | | |
| | **Upstream** | https://huggingface.co/mradermacher/maya1-GGUF | | |
| ## Quick start | |
| ```bash | |
| hf download eugenehp/maya1 --local-dir . | |
| cargo run -p rlx-maya1 --release -- --weights maya1.Q4_K_M.gguf | |
| ``` | |
| ## File highlights | |
| - `maya1.Q4_K_M.gguf` (1.9 GiB) | |
| - `tokenizer.json` (21.8 MiB) | |
| ## Run with RLX | |
| Clone [rlx-models](https://github.com/MIT-RLX/rlx-models), place this repo under `weights/tts/maya1` (or pass the path explicitly), then: | |
| ```bash | |
| cargo run -p rlx-maya1 --release -- --weights maya1.Q4_K_M.gguf | |
| ``` | |
| ## License | |
| Apache License 2.0 — see `LICENSE`. Inherit upstream terms when redistributing. | |
| Original weights and authorship: https://huggingface.co/mradermacher/maya1-GGUF | |
| ## 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. | |