Add docker compose and llama-cpp-server (#21)
* Add GPU Dockerfile and all-in-one llama-cpp docker compose w/ model downloader (fixed) * Sets `.env` file in docker compose + uses env values for llama-cpp-server
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6 changed files with 165 additions and 10 deletions
3
.dockerignore
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.dockerignore
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*.env
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models/
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*.gguf
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.gitignore
vendored
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.gitignore
vendored
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.env
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__pycache__/
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.venv/
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venv/
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models/
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*.gguf
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47
Dockerfile.gpu
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47
Dockerfile.gpu
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FROM ubuntu:22.04
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# Set non-interactive frontend
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ENV DEBIAN_FRONTEND=noninteractive
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# Install Python and other dependencies
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RUN apt-get update && apt-get install -y \
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python3.10 \
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python3-pip \
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python3-venv \
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libsndfile1 \
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ffmpeg \
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portaudio19-dev \
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&& apt-get clean && rm -rf /var/lib/apt/lists/*
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# Create non-root user and set up directories
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RUN useradd -m -u 1001 appuser && \
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mkdir -p /app/outputs /app && \
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chown -R appuser:appuser /app
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USER appuser
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WORKDIR /app
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# Copy dependency files
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COPY --chown=appuser:appuser requirements.txt ./requirements.txt
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# Create and activate virtual environment
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RUN python3 -m venv /app/venv
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ENV PATH="/app/venv/bin:$PATH"
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# Install PyTorch with CUDA support and other dependencies
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RUN pip3 install --no-cache-dir torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124 && \
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pip3 install --no-cache-dir -r requirements.txt
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# Copy project files
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COPY --chown=appuser:appuser . .
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# Set environment variables
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ENV PYTHONUNBUFFERED=1 \
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PYTHONPATH=/app \
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USE_GPU=true
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# Expose the port
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EXPOSE 5005
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# Run FastAPI server with uvicorn
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CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "5005", "--workers", "1"]
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25
README.md
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README.md
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@ -57,6 +57,8 @@ Listen to sample outputs with different voices and emotions:
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```
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Orpheus-FastAPI/
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├── app.py # FastAPI server and endpoints
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├── docker-compose.yml # Docker compose configuration
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├── Dockerfile.gpu # GPU-enabled Docker image
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├── requirements.txt # Dependencies
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├── static/ # Static assets (favicon, etc.)
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├── outputs/ # Generated audio files
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@ -74,9 +76,21 @@ Orpheus-FastAPI/
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- Python 3.8-3.11 (Python 3.12 is not supported due to removal of pkgutil.ImpImporter)
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- CUDA-compatible GPU (recommended: RTX series for best performance)
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- Separate LLM inference server running the Orpheus model (e.g., LM Studio or llama.cpp server)
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- Using docker compose or separate LLM inference server running the Orpheus model (e.g., LM Studio or llama.cpp server)
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### Installation
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### 🐳 Docker compose
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The docker compose file orchestrates the Orpheus-FastAPI for audio and a llama.cpp inference server for the base model token generation. The GGUF model is downloaded with the model-init service.
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```bash
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cp .env.example .env # Nothing needs to be changed, but the file is required
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```
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```bash
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docker compose up --build
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```
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### FastAPI Service Native Installation
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1. Clone the repository:
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```bash
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@ -271,7 +285,7 @@ You can easily integrate this TTS solution with [OpenWebUI](https://github.com/o
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### External Inference Server
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This application requires a separate LLM inference server running the Orpheus model. You can use:
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This application requires a separate LLM inference server running the Orpheus model. For easy setup, use Docker Compose, which automatically handles this for you. Alternatively, you can use:
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- [GPUStack](https://github.com/gpustack/gpustack) - GPU optimised LLM inference server (My pick) - supports LAN/WAN tensor split parallelisation
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- [LM Studio](https://lmstudio.ai/) - Load the GGUF model and start the local server
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@ -291,9 +305,9 @@ The inference server should be configured to expose an API endpoint that this Fa
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### Environment Variables
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You can configure the system using environment variables or a `.env` file:
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Configure in docker compose, if using docker. Not using docker; create a `.env` file:
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- `ORPHEUS_API_URL`: URL of the LLM inference API (tts_engine/inference.py)
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- `ORPHEUS_API_URL`: URL of the LLM inference API (default in Docker: http://llama-cpp-server:5006/v1/completions)
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- `ORPHEUS_API_TIMEOUT`: Timeout in seconds for API requests (default: 120)
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- `ORPHEUS_MAX_TOKENS`: Maximum tokens to generate (default: 8192)
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- `ORPHEUS_TEMPERATURE`: Temperature for generation (default: 0.6)
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@ -301,6 +315,7 @@ You can configure the system using environment variables or a `.env` file:
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- `ORPHEUS_SAMPLE_RATE`: Audio sample rate in Hz (default: 24000)
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- `ORPHEUS_PORT`: Web server port (default: 5005)
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- `ORPHEUS_HOST`: Web server host (default: 0.0.0.0)
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- `ORPHEUS_MODEL_NAME`: Model name for inference server
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The system now supports loading environment variables from a `.env` file in the project root, making it easier to configure without modifying system-wide environment settings. See `.env.example` for a template.
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24
app.py
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app.py
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@ -11,14 +11,30 @@ from dotenv import load_dotenv
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# Function to ensure .env file exists
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def ensure_env_file_exists():
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"""Create a default .env file if one doesn't exist"""
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"""Create a .env file from defaults and OS environment variables"""
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if not os.path.exists(".env") and os.path.exists(".env.example"):
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try:
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# Copy .env.example to .env
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# 1. Create default env dictionary from .env.example
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default_env = {}
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with open(".env.example", "r") as example_file:
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for line in example_file:
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line = line.strip()
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if line and not line.startswith("#") and "=" in line:
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key = line.split("=")[0].strip()
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default_env[key] = line.split("=", 1)[1].strip()
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# 2. Override defaults with Docker environment variables if they exist
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final_env = default_env.copy()
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for key in default_env:
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if key in os.environ:
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final_env[key] = os.environ[key]
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# 3. Write dictionary to .env file in env format
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with open(".env", "w") as env_file:
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env_file.write(example_file.read())
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print("✅ Created default configuration file at .env")
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for key, value in final_env.items():
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env_file.write(f"{key}={value}\n")
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print("✅ Created default .env file from .env.example and environment variables.")
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except Exception as e:
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print(f"⚠️ Error creating default .env file: {e}")
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68
docker-compose.yml
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docker-compose.yml
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services:
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orpheus-fastapi:
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container_name: orpheus-fastapi
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build:
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context: .
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dockerfile: Dockerfile.gpu
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ports:
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- "5005:5005"
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env_file:
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- .env
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environment:
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- ORPHEUS_API_URL=http://llama-cpp-server:5006/v1/completions
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: all
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capabilities: [gpu]
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restart: unless-stopped
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depends_on:
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llama-cpp-server:
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condition: service_started
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llama-cpp-server:
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image: ghcr.io/ggml-org/llama.cpp:server-cuda
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ports:
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- "5006:5006"
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volumes:
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- ./models:/models
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env_file:
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- .env
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depends_on:
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model-init:
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condition: service_completed_successfully
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: all
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capabilities: [gpu]
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restart: unless-stopped
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command: >
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-m /models/${ORPHEUS_MODEL_NAME}
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--port 5006
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--host 0.0.0.0
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--n-gpu-layers 29
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--ctx-size ${ORPHEUS_MAX_TOKENS}
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--n-predict ${ORPHEUS_MAX_TOKENS}
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--rope-scaling linear
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model-init:
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image: curlimages/curl:latest
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user: ${UID}:${GID}
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volumes:
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- ./models:/app/models
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working_dir: /app
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command: >
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sh -c '
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if [ ! -f /app/models/${ORPHEUS_MODEL_NAME} ]; then
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echo "Downloading model file..."
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wget -P /app/models https://huggingface.co/lex-au/${ORPHEUS_MODEL_NAME}/resolve/main/${ORPHEUS_MODEL_NAME}
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else
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echo "Model file already exists"
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fi'
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restart: "no"
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