ezlocalai 0.1.12

ezlocalai is an easy to set up local multimodal artificial intelligence server with OpenAI Style Endpoints.

ezlocalai

ezlocalai is an easy set up artificial intelligence server that allows you to easily run multimodal artificial intelligence from your computer. It is designed to be as easy as possible to get started with running local models. It automatically handles downloading the model of your choice and configuring the server based on your CPU, RAM, and GPU specifications. It also includes OpenAI Style endpoints for easy integration with other applications using ezlocalai as an OpenAI API proxy with any model. Additional functionality is built in for voice cloning text to speech and a voice to text for easy voice communication as well as image generation entirely offline after the initial setup.

Prerequisites

• Git
• Docker Desktop (Windows or Mac)
• CUDA Toolkit (NVIDIA GPU only)

Additional Linux Prerequisites

• Docker
• Docker Compose
• NVIDIA Container Toolkit (NVIDIA GPU only)

Installation

git clone https://github.com/DevXT-LLC/ezlocalai
cd ezlocalai

Environment Setup

Expand Environment Setup if you would like to modify the default environment variables, otherwise skip to Usage. All environment variables are optional and have useful defaults. Change the default model that starts with ezlocalai in your .env file.

Environment Setup (Optional)

None of the values need modified in order to run the server. If you are using an NVIDIA GPU, I would recommend setting the GPU_LAYERS and MAIN_GPU environment variables. If you plan to expose the server to the internet, I would recommend setting the EZLOCALAI_API_KEY environment variable for security. THREADS is set to your CPU thread count minus 2 by default, if this causes significant performance issues, consider setting the THREADS environment variable manually to a lower number.

Modify the .env file to your desired settings. Assumptions will be made on all of these values if you choose to accept the defaults.

Replace the environment variables with your desired settings. Assumptions will be made on all of these values if you choose to accept the defaults.

• EZLOCALAI_URL - The URL to use for the server. Default is http://localhost:8091.
• EZLOCALAI_API_KEY - The API key to use for the server. If not set, the server will not require an API key when accepting requests.
• NGROK_TOKEN - The ngrok token to use for the server. If not set, ngrok will not be used. Using ngrok will allow you to expose your ezlocalai server to the public with as simple as an API key. Get your free NGROK_TOKEN here.
• DEFAULT_MODEL - The default model to use when no model is specified. Use the Hugging Face path. Default is TheBloke/phi-2-dpo-GGUF.
• LLM_MAX_TOKENS - The maximum number of tokens to use for the language model. If set to 0, it will automatically use the max tokens for the model. Default is 0.
• WHISPER_MODEL - The model to use for speech-to-text. Default is base.en.
• AUTO_UPDATE - Whether or not to automatically update ezlocalai. Default is true.
• THREADS - The number of CPU threads ezlocalai is allowed to use. Default is 4.
• GPU_LAYERS (Only applicable to NVIDIA GPU) - The number of layers to use on the GPU. Default is 0. Your GPU_LAYERS will automatically determine a number of layers to use based on your GPU's memory if it is set to -1 and you have an NVIDIA GPU. If it is set to -2, it will use the maximum number of layers requested by the model.
• MAIN_GPU (Only applicable to NVIDIA GPU) - The GPU to use for the language model. Default is 0.
• IMG_ENABLED - If set to true, models will choose to generate images when they want to based on the user input. This is only available on GPU. Default is false.
• SD_MODEL - The stable diffusion model to use. Default is stabilityai/sdxl-turbo.
• VISION_MODEL - The vision model to use. Default is None. Current options are deepseek-ai/deepseek-vl-1.3b-chat and deepseek-ai/deepseek-vl-7b-chat.

Usage

NVIDIA GPU

docker-compose -f docker-compose-cuda.yml down
docker-compose -f docker-compose-cuda.yml build
docker-compose -f docker-compose-cuda.yml up

CPU

docker-compose down
docker-compose build
docker-compose up

OpenAI Style Endpoint Usage

OpenAI Style endpoints available at http://<YOUR LOCAL IP ADDRESS>:8091/v1/ by default. Documentation can be accessed at that http://localhost:8091 when the server is running.

For examples on how to use the server to communicate with the models, see the Examples Jupyter Notebook once the server is running. We also have an example to use in Google Colab.

Demo UI

You can access the basic demo UI at http://localhost:8502, or your local IP address with port 8502.

Workflow

graph TD
   A[app.py] --> B[FASTAPI]
   B --> C[Pipes]
   C --> D[LLM]
   C --> E[STT]
   C --> F[CTTS]
   C --> G[IMG]
   D --> H[llama_cpp]
   D --> I[tiktoken]
   D --> J[torch]
   E --> K[faster_whisper]
   E --> L[pyaudio]
   E --> M[webrtcvad]
   E --> N[pydub]
   F --> O[TTS]
   F --> P[torchaudio]
   G --> Q[diffusers]
   Q --> J
   A --> R[Uvicorn]
   R --> S[ASGI Server]
   A --> T[API Endpoint: /v1/completions]
   T --> U[Pipes.get_response]
   U --> V{completion_type}
   V -->|completion| W[LLM.completion]
   V -->|chat| X[LLM.chat]
   X --> Y[LLM.generate]
   W --> Y
   Y --> Z[LLM.create_completion]
   Z --> AA[Return response]
   AA --> AB{stream}
   AB -->|True| AC[StreamingResponse]
   AB -->|False| AD[JSON response]
   U --> AE[Audio transcription]
   AE --> AF{audio_format}
   AF -->|Exists| AG[Transcribe audio]
   AG --> E
   AF -->|None| AH[Skip transcription]
   U --> AI[Audio generation]
   AI --> AJ{voice}
   AJ -->|Exists| AK[Generate audio]
   AK --> F
   AK --> AL{stream}
   AL -->|True| AM[StreamingResponse]
   AL -->|False| AN[JSON response with audio URL]
   AJ -->|None| AO[Skip audio generation]
   U --> AP[Image generation]
   AP --> AQ{IMG enabled}
   AQ -->|True| AR[Generate image]
   AR --> G
   AR --> AS[Append image URL to response]
   AQ -->|False| AT[Skip image generation]
   A --> AU[API Endpoint: /v1/chat/completions]
   AU --> U
   A --> AV[API Endpoint: /v1/embeddings]
   AV --> AW[LLM.embedding]
   AW --> AX[LLM.create_embedding]
   AX --> AY[Return embedding]
   A --> AZ[API Endpoint: /v1/audio/transcriptions]
   AZ --> BA[STT.transcribe_audio]
   BA --> BB[Return transcription]
   A --> BC[API Endpoint: /v1/audio/generation]
   BC --> BD[CTTS.generate]
   BD --> BE[Return audio URL or base64 audio]
   A --> BF[API Endpoint: /v1/models]
   BF --> BG[LLM.models]
   BG --> BH[Return available models]
   A --> BI[CORS Middleware]
   BJ[.env] --> BK[Environment Variables]
   BK --> A
   BL[setup.py] --> BM[ezlocalai package]
   BM --> BN[LLM]
   BM --> BO[STT]
   BM --> BP[CTTS]
   BM --> BQ[IMG]
   A --> BR[API Key Verification]
   BR --> BS[verify_api_key]
   A --> BT[Static Files]
   BT --> BU[API Endpoint: /outputs]
   A --> BV[Ngrok]
   BV --> BW[Public URL]