KitOps Release v0.2–Introducing Dev Mode and the ability to chain ModelKits
Welcome KitOps v0.2! This update brings two major features for working with LLMs, as well numerous smaller enhancements.
Welcome KitOps v0.2! This update brings two major features for working with LLMs, as well numerous smaller enhancements.
Dive into the world of large language models with our step-by-step tutorial on fine-tuning using LoRA, powered by tools like llama.cpp and KitOps. LoRA (Low-Rank Adaptation) is an efficient technique for adapting pre-trained models, minimizing computational overhead. We’ll guide you through setting up your environment, creating a Kitfile, building a LoRA adapter, and deploying your fine-tuned model. By the end, you’ll have a packaged model ready for deployment.
In AI projects the biggest source of friction are the handoffs between data scientists, application developers, testers, and infrastructure engineers as the project moves from development to production.
ModelKits, much like other OCI artifacts, can be identified using tags that are comprehensible to humans. This blog explores various strategies for effectively tagging your ModelKits.
Our latest contribution to KitOps is a GitHub Action that simplifies integrating Kit CLI into your existing CI/CD toolset.
AI/ML is a wildfire of a trend. It’s being integrated into just about every application you can think of. When compared to other technical innovations over the past years, like the blockchain, the infrastructure and tooling was being built (and still is) in parallel to discovering a meaningful application.