Liquid AI, a startup co-founded by former researchers from the Massachusetts Institute of Technology (MIT)’s Computer Science and Artificial Intelligence Laboratory (CSAIL), has announced the debut of its first multimodal AI models: the “Liquid Foundation Models (LFMs).” Unlike most others of the current generative AI wave, these models are not based around the transformer architecture outlined in the seminal 2017 paper “Attention Is All You Need.” Instead, Liquid states that its goal “is to explore ways to build foundation models beyond Generative Pre-trained Transformers (GPTs)” and with the new LFMs, specifically building from “first principles…the same way engineers built engines, cars, and airplanes.” It seems they’ve done just that — as the new LFM models already boast superior performance to other transformer-based ones of comparable size such as Meta’s Llama 3.1-8B and Microsoft’s Phi-3.5 3.8B. Liquid’s LFMs currently come in three different sizes and variants:
- LFM 1.3B (smallest)
- LFM 3B
- LFM 40B MoE (largest, a “Mixture-of-Experts” model similar to Mistral’s Mixtral)
The “B” in their name stands for billion and refers the number of parameters — or settings — that govern the model’s information processing, analysis, and output generation. Generally, models with a higher number of parameters are more capable across a wider range of tasks.
