Technical Content
Mozilla.ai developed and released the OpenStreetMap AI Helper Blueprint. If you love maps and are interested in training your own computer vision model, you’ll enjoy diving into this Blueprint.
Technical Content
Previously, we explored how LLMs like Meta’s Llama reshaped AI, offering transparency and control. We discussed open-weight models like DeepSeek and deployment options. Now, we show how to deploy DeepSeek V3, a powerful open-weight model, on a Kubernetes cluster using vLLM.
Technical Content
The landscape of LLMs has evolved dramatically since ChatGPT burst onto the scene in late 2022. At Mozilla.ai, we’re focused on improving trust in open-source AI by supporting their use in appropriate situations and their proper evaluation.
Technical Content
When deciding on a new Blueprint, we focus on selecting an end application that is both practical and impactful, along with the best techniques to implement it. With endless possible applications of LMs today, selecting one that is actually useful can be challenging.
Product Release
Lumigator is a developer-first tool designed and built by the community to help engineers evaluate and compare AI models with ease. Lumigator empowers developers to make data-driven choices when integrating AI models into their applications.
Technical Content
Blueprints are customizable workflows that help developers build AI applications using open-source tools and models. In this blog, we’ll dive into our first Blueprint: document-to-podcast. We’ll explain how it works, our technical decisions, and how you can use and customize it yourself.
Product Release
Developers today face many challenges when trying to integrate AI into their apps or building an “AI solution” from scratch. At Mozilla.ai, we’re committed to breaking down these barriers with Blueprints – our initiative to help developers adopt open-source AI tools and models with confidence.
Technical Content
Lumigator 🐊 is a self-hosted, open-source Python application for evaluating large language models using offline metrics. It targets common machine learning use-cases, starting with summarization, and is extensible at the task and job level.
Technical Content
The behavior of ML models is often affected by randomness at different levels, from the initialization of model parameters to the dataset split into training and evaluation. Thus, predictions made by a model are potentially different every time you run it.
Product Release
With hundreds of models, each offering different capabilities, the process can be overwhelming. That’s why Mozilla.ai is developing Lumigator, a product designed to help developers confidently select the best LLM for their specific project.
Expert Opinion
We are witnessing explosive growth in the development of artificial intelligence (AI) applications across various industries, from virtual agents to healthcare diagnostics and autonomous driving.
Expert Opinion
At the TAUS Massively Multilingual AI conference in Rome, I had the honor of moderating a panel on Multilingual AI provocatively titled “No English Please - How to move towards truly multilingual AI”. In this panel, we explored the pressing need for AI to transcend linguistic barriers and embrace global diversity.