The article also highlights the key offerings of AIKit, including extensive inference capabilities, an extensible interface for fine-tuning, and compatibility with various model formats and the OpenAI API. It further explains how to run inference on a local machine, fine-tune a model with a dataset to create a custom model, and run that model to get a response. The next parts of the series will cover automating and scaling the workflow using GitHub Actions and Kubernetes, and addressing the security implications of running LLM workloads in production.
Key takeaways:
- AIKit is a comprehensive cloud-native, vendor-agnostic solution designed for developers looking to fine-tune, build, and deploy large language models (LLMs) with ease.
- AIKit offers extensive inference capabilities across various formats and provides an extensible interface for a fast, memory-efficient, and straightforward fine-tuning experience.
- The article provides a step-by-step guide on how to set up AIKit on your local machine, perform inference and fine-tuning tasks, and create a robust, automated workflow for AI projects.
- The article also addresses the security implications of running LLM workloads in production, discussing how AIKit tackles vulnerabilities, ensures model security, and supports air-gapped environments.