AIKit

A one-stop solution for hosting, deploying, building, and fine-tuning open-source large language models.

CommonProductProgrammingOpen-sourceLarge Language Models

AIKit is an open-source tool designed to simplify the process of hosting, deploying, building, and fine-tuning large language models (LLMs). It offers a REST API compatible with the OpenAI API, supporting various inference capabilities and formats, allowing users to send requests using any compatible client. Furthermore, AIKit provides an extensible fine-tuning interface with Unsloth support, offering users a fast, memory-efficient, and user-friendly fine-tuning experience.

Wide-ranging inference capabilities through LocalAI
compatible with OpenAI API.
Extensible fine-tuning interface with Unsloth support.
Runs effortlessly on Docker without requiring GPUs
internet access
or additional tools.
Minimized image size
reducing vulnerabilities and attack surface.
Supports declarative configuration
simplifying inference and fine-tuning processes.

AIKit is suitable for developers
data scientists
and machine learning engineers who need a simple
efficient
and cost-effective way to deploy and fine-tune large language models. It provides robust support whether you're running it on a local machine or in a cloud environment.

Quickly start LLMs on a local machine with AIKit without needing a GPU.
Deploy LLMs on Kubernetes with AIKit for automated deployment and management.
Fine-tune models on specific domain data using AIKit's fine-tuning capabilities.

1. Install Docker to run AIKit.
2. Clone the AIKit repository from GitHub or use the pre-built Docker image.
3. Configure AIKit's inference and fine-tuning parameters as needed.
4. Start the AIKit service using Docker commands.
5. Send requests using an OpenAI-compatible API client.

Visit

AIKit Visit Over Time

Monthly Visits

503747431

Bounce Rate

37.31%

Page per Visit

5.7

Visit Duration

00:06:44

AIKit Visit Trend

AIKit Visit Geography

AIKit Traffic Sources

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