I'm a Machine Learning Engineer at Mozilla.ai. I enjoy a good engineering puzzle alongside a cup of coffee, and do my best to stay up to date on all the latest happenings in the world of AI.
After OpenAI’s ChatGPT release, the default standard for communication was the OpenAI Completion API. However, with the new “reasoning models”, a critical piece of the output isn’t handled by that OpenAI specification, leaving each provider to decide how to handle the new model capabilities.
When it comes to using LLMs, it’s not always a question of which model to use: it’s also a matter of choosing who provides the LLM and where it is deployed. Today, we announce the release of any-llm, a Python library that provides a simple unified interface to access the most popular providers.
Since the launch of ChatGPT in 2022, generative AI and LLMs have rapidly entered everyday life. The viral adoption of these tools was unprecedented, and in some ways contentious. In order to grant greater capabilities to LLMs, they can be integrated into a framework that’s referred to as an “Agent”.
New state-of-the-art models emerge every few weeks, making it hard to keep up, especially when testing and integrating them. In reality, many available models may already meet our needs. The key question isn’t “Which model is the best?” but rather, “What’s the smallest model that gets the job done?”
A typical user may be building a summarization application for their domain and wondering: “Do I need to go for a model as big as DeepSeek, or can I get away with a smaller model?”. This takes us to the key elements: Metrics, Models, and Datasets.
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.