Arthur Mensch, co-founder of Mistral and co-author of the influential 'Chinchilla' paper, shares insights on the AI landscape with Anjney Midha. They discuss the misconceptions surrounding open-source technology and emphasize its crucial role in AI innovation. The conversation highlights key advancements like Mistral-7B and Mixtral, showcasing how community-driven development enhances efficiency and fosters rapid progress. Mensch also addresses the ongoing debate between open and closed models, advocating for transparency and collaboration to ensure safety and eliminate biases.
Data is more important than sheer model size in training large language models, as shown by the Chinchilla paper.
Mistral's open source models like Mistral-7B and PIXTRAL provide developers with cost-efficient and faster alternatives to closed models, offering more control, efficiency, and affordability.
Deep dives
Scaling Laws and the Importance of Data
The podcast discusses the evolution of large language models and the scaling laws that have governed their development. It highlights the misconception that model size alone determines performance, emphasizing the role of data in training. The pivotal Tinchilla paper by Arthur Metch and others challenged the prevailing scaling laws and showed that datasets are more important than sheer model size. This understanding led Arthur Metch, Guillaume Lample, and Timothy LaQuar to found Mistral and release state-of-the-art open source models like Mistral 7B and the newly introduced PIXTRAL, which offer developers more cost-efficient and faster alternatives to closed models.
The Story of Mistral's Founding Team
Arthur Metch, Guillaume Lample, and Timothy LaQuar, who had previous experience working together, came together to form Mistral. After realizing the limitations of scaling laws and the importance of data in large language models, they saw an opportunity to create a small team focused on open source development. They left their respective companies and began working on developing the Mistral models, such as Chinchilla and now PIXTRAL, with the aim of providing developers with more control, greater efficiency, and improved affordability.
Mistral's Mixture of Experts Model
Mistral's latest model is a mixture of experts (MIXTRAL) architecture, which improves inference efficiency compared to dense models. MIXTRAL duplicates the dense layers of the transformer model and uses a routing mechanism to direct tokens to specific expert layers. By executing only a subset of experts per layer, MIXTRAL reduces the number of parameters executed while maintaining performance. With models like MIXTRAL and Mistral 7B, Mistral aims to offer developers cost-effective, high-performance alternatives to closed models, making them suitable for various tasks and applications.
The Battle Between Closed and Open Source Models
The podcast explores the battle for the neutrality of technology and the ongoing debate between closed and open source models. Mistral advocates for an open source approach, citing the benefits of increased collaboration, knowledge sharing, and innovation within the research community. They argue that open source models can be just as safe as closed models and allow for independent scrutiny and oversight. Additionally, Mistral emphasizes the need to regulate applications built on large language models rather than the models themselves, and highlights the importance of measuring performance based on application-specific needs rather than arbitrary benchmarks like flops.
Arthur Mensch is the co-founder of Mistral and the co-author of Deepmind’s pivotal 2022 "Chinchilla" paper.
In September 2023, Mistral released Mistral-7B, an advanced open-source language model that has rapidly become the top choice for developers. Just this week, they introduced a new mixture of experts model – Mixtral — that’s already generating significant buzz among AI developers.
As the battleground around large language models heats up, join us for a conversation with Arthur as he sits down with a16z General Partner Anjney Midha. Together, they delve into the misconceptions and opportunities around open source; the current performance reality of open and closed models; and the compute, data, and algorithmic innovations required to efficiently scale LLMs.
Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
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