Technical Content
Uncovering GenAI Trends: Using Local Language Models to Explore 35 Organizations
Over the past few months at Mozilla.ai, we’ve engaged with a number of organizations to learn how they are using language models in practice.
Technical Content
Technical Content
On model selection for text summarization
We want to help users by providing an easy way for them to a) evaluate whatever model they choose to use, b) get understandable and transparent results, and c) trust the tech stack they want to build. In short, we’re offering accessibility, transparency, and trust.
Technical Content
Local LLM-as-judge evaluation with lm-buddy, Prometheus and llamafile
In the bustling AI news cycle, where new models are unveiled at every turn, cost and evaluation don’t come up as frequently but are crucial to both developers and businesses in their use of AI systems. It is well known that LLMs are extremely costly to pre-train; but closed
Technical Content
The cost of cutting-edge: Scaling compute and limiting access
In a year marked by extraordinary advancements in artificial intelligence, largely driven by the evolution of large language models (LLMs), one factor stands out as a universal accelerator: the exponential growth in computational power.
Technical Content
LLM evaluation at scale with the NeurIPS Large Language Model Efficiency Challenge
After a year of breakneck innovation and hype in the AI space, we have now moved sufficiently beyond the peak of the hype cycle to start asking a critical question: are LLMs good enough yet to solve all of the business and societal challenges we are setting them up for?