The video introduces Liquid AI’s new generative AI models, which feature a unique architecture and demonstrate strong benchmark performance, particularly the 40 billion parameter model that utilizes a mixture of experts for efficiency. However, real-world tests reveal inconsistencies in the model’s performance, suggesting that benchmarks may not accurately reflect practical capabilities, leading to disappointment in its overall effectiveness.
The video introduces a new generative AI model from Liquid AI, which features a novel architecture distinct from the widely used Transformers architecture. The presenter discusses the Liquid Foundation Models, which come in three sizes: 1 billion, 3 billion, and 40 billion parameters. These models are designed to perform exceptionally well on various benchmarks, showcasing their capabilities in comparison to other models in the same size class. The 40 billion parameter model is particularly noteworthy as it employs a mixture of experts, allowing it to utilize only a fraction of its parameters during inference, enhancing its efficiency.
The presenter shares benchmark results, highlighting the performance of the Liquid Foundation Models against competitors like Llama 3.2 and other established models. The 1 billion and 3 billion parameter models demonstrate strong performance, particularly in the MMLU Pro benchmark, while the 40 billion model excels in various tasks despite its smaller active parameter count. The video emphasizes the memory efficiency of the Liquid models, which maintain a low memory footprint even when generating large outputs, a significant advantage over other models that experience a steep increase in memory usage.
Despite the promising benchmarks, the presenter conducts a series of tests to evaluate the model’s real-world performance. The first test involves generating a Tetris game in Python, which the model fails to complete successfully. Subsequent tests include logical reasoning and math problems, where the model performs inconsistently, passing some questions while failing others. The presenter notes that while the model can achieve correct answers, its reasoning and explanations often lack clarity or accuracy.
The video continues with additional tests, including questions about word counts and moral dilemmas, where the model struggles to provide satisfactory responses. The presenter expresses disappointment in the model’s performance, suggesting that the benchmarks may not accurately reflect its capabilities in practical applications. This raises questions about the effectiveness of non-Transformer-based models, as the presenter recalls previous experiences with similar architectures that did not meet expectations.
In conclusion, while the Liquid Foundation Models show promise in benchmark tests, their real-world performance leaves much to be desired. The presenter remains hopeful for future advancements in non-Transformer models but concludes that the current iteration does not live up to its potential. The video ends with a call to action for viewers to like and subscribe for more content, indicating the presenter’s commitment to exploring and evaluating AI developments.