An easy way to deploy local LLM
On-premises LLM deployment: an overview of open-source solutions
Introduction
Large Language Models (LLM) have become indispensable tools for developers and researchers. They can be used to solve a variety of problems. However, using such models is often associated with a dependence on external services, which imposes certain limitations. Local deployment of LLM allows you to maintain control over the data and flexibly customize the model for specific needs, while taking advantage of the local infrastructure.
In this article I would like to talk about the benefits of local deployment of LLM and consider several open-source solutions that can be used for this purpose.
Why deploy LLM on-premises?
Deploying large language models locally has several key benefits:
No dependence on third-party services: Working locally means you can avoid external APIs, which is especially important for projects with high privacy requirements or in situations where internet connectivity is limited.
Improved data protection: All data remains within your infrastructure, reducing the risk of leaks and increasing security, which is especially important for organizations working with confidential information.
Flexibility of customization: Local use of models allows you to customize them for specific tasks and even further train them on company data, thereby improving the quality of the results.
Optimizing resource usage: You can optimize the use of available computing power, whether it's GPU, CPU, or other resources, which can significantly improve performance.
Review of popular open-source solutions
There are several popular open-source projects for local deployment of LLM. Let's consider the main ones:
LocalAI:
Description: LocalAI is a project that allows you to run language models locally with minimal setup. It supports a variety of models and integrates easily with other tools.
Key features:
Supports various model formats, including models from Hugging Face and other sources.
Easy to use thanks to the REST API, which allows you to quickly integrate it into different applications.
The ability to fine-tune and further train models, which allows them to be adapted to specific tasks.
Supports hardware acceleration using GPU for improved performance.
Advantages: Easy to use and install, flexible in choosing models, ability to work on different platforms.
Flaws: May require significant resources to handle large models.
AnythingLLM:
Description: AnythingLLM provides a general-purpose approach to running language models, supporting a variety of architectures and offering a high degree of flexibility in customization.
Key features:
Flexibility in customizing models, including the ability to load and use any available models, including your own.
Supports multiple architectures such as GPT, BERT and others.
Modular architecture that allows you to add additional features and modules as needed.
Intuitive interface and detailed documentation simplify the installation and use process.
Advantages: Wide support for various models, flexible customization for specific needs.
Flaws: May require more time and experience to fully utilize.
Ollama:
Description: Ollama is a solution focused on ease of installation and use, providing minimal setup and management requirements.
Advantages: Quick installation and launch, minimal configuration requirements.
Flaws: Less options for fine-tuning compared to more comprehensive solutions.
Hugging Face Transformers:
Description: The Hugging Face library provides access to a large number of pre-trained models and tools for tuning and using them.
Advantages: Support for multiple models and languages, active development and a large user community.
Flaws: Requires significant computing power to handle large models.
How to Quickly Deploy LocalAI and AnythingLLM with Docker Compose
If you want to quickly get started with LocalAI and AnythingLLM, Docker Compose is a great tool that makes the deployment process much easier. Here's an example file docker-compose.ymlwhich will help you quickly set up your environment:
yamlCopy codeversion: "3.9"services: anythingllm: image: mintplexlabs/anythingllm container_name: anythingllm ports: - 3001:3001 cap_add: - SYS_ADMIN volumes: - ${STORAGE_LOCATION}:/app/server/storage - ${STORAGE_LOCATION}/.env:/app/server/.env environment: - STORAGE_DIR=/app/server/storage api: image: localai/localai:latest-aio-cpu # Для конкретной версии: # image: localai/localai:v2.20.1-aio-cpu # Для Nvidia GPUs раскомментируйте одну из следующих строк (cuda11 или cuda12): # image: localai/localai:v2.20.1-aio-gpu-nvidia-cuda-11 # image: localai/localai:v2.20.1-aio-gpu-nvidia-cuda-12 # image: localai/localai:latest-aio-gpu-nvidia-cuda-11 # image: localai/localai:latest-aio-gpu-nvidia-cuda-12 healthcheck: test: ["CMD", "curl", "-f", "http://localhost:8080/readyz"] interval: 1m timeout: 20m retries: 5 ports: - 8080:8080 environment: - DEBUG=true # ... volumes: - ./models:/build/models:cached # Раскомментируйте следующую часть, если используете Nvidia GPUs # deploy: # resources: # reservations: # devices: # - driver: nvidia # count: 1 # capabilities: [gpu]Create a file as well .envto set environment variables:
bashCopy codeSTORAGE_LOCATION=$HOME/anythingllmSteps to start:
Download or create
docker-compose.ymlAnd.envin one catalog.Set up variables in the file
.envto suit your needs.In the terminal, run the command
docker-compose up -dbeing in the directory withdocker-compose.yml.
These steps will help you quickly deploy LocalAI and AnythingLLM on your machine, allowing you to work with large language models without depending on external APIs.
Conclusion
Deploying LLM locally offers many benefits, especially in terms of data privacy and customization flexibility. There are various open-source solutions, such as LocalAI, AnythingLLM, Ollama, and Hugging Face Transformers, that suit different scenarios and needs. With Docker Compose, deploying these tools is easier than ever.
Use local LLM for your projects and enjoy freedom of customization and privacy!
