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MLOps.community

LLMs in Focus: From One-Size Fits All to Verticalized Solutions // Venky Ganti & Laurel Orr // #196

Dec 19, 2023
55:14
Snipd AI
A discussion with Venky Ganti, CEO of Numbers Station, and Laurel Orr about the merits and limitations of 'one-size fits all' LLMs, the contrast between general LLMs and verticalized models, the significance of ownership structures, and the future of LLMs. They also explore the challenges of hosting private LLMs, AI's role in data wrangling, customizing LLMs for complex data scenarios, the evolution of LLMs and the need for customization, and evaluation methods for text-to-sequel models.
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Podcast summary created with Snipd AI

Quick takeaways

  • The future of LLMs involves customization and personalization, tailoring models to specific use cases and exploring the trade-offs between larger and smaller models.
  • Democratization of data recipes aims to make high-quality training data sets more accessible, enabling open-source models to compete with proprietary models.

Deep dives

Advancements in LLMs and Customization

The future of LLMs is likely to see advancements in customization and personalization. Rather than relying on a single large model like GPT-4, the trend will be towards tailoring models to specific use cases and fine-tuning them to achieve higher accuracy. This will involve exploring different sizes of models, context lengths, and understanding the trade-offs between larger models and smaller, more focused models. The availability of more affordable hardware will allow for hosting and training local models, enabling individuals and organizations to have more control and customization over their models.

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