Name: Enhanced base LLMs: Perplexity Sonar+DeepSeek R1 optimized by fine tuning Llama
Start: 2025-02-27T18:00:00-08:00
End: 2025-02-27T20:00:00-08:00

[FounderCulture](http://FounderCulture.net), [Storytell](http://Storytell.ai) and [UpHonest Capital](https://www.uphonestcapital.com/) have teamed up to create a Meetup series called "Building The Future of AI."

**🟢 This event will be live in San Mateo, CA -- [also be live-streamed on this Zoom link](https://go.storytell.ai/drodio-zoom)** if you can't join in person. The Zoom will start at 6:30pm PT.

**Join us for a robust discussion led by [Tim Kellogg](https://www.linkedin.com/in/tim-kellogg-69802913/)** and moderated by [DROdio](http://linkedin.com/in/drodio).

**Enhanced base LLMs: Perplexity Sonar+DeepSeek R1 optimized by fine tuning Llama**

The rapid evolution of large language models has brought fine-tuning to the forefront as a critical strategy for enhancing performance. This session will explore the cutting-edge approaches used to optimize two leading models: Sonar and DeepSeek R1, both fine-tuned on Llama architectures.

* **Perplexity's Sonar**, built on Llama 3.3 70B, has been further trained to excel in factuality and readability, making it ideal for search applications. Its integration with Cerebras inference technology enables blazing-fast token processing, allowing for real-time responses. The model’s open-source nature and Mixture of Experts (MoE) architecture, which activates only a subset of parameters per query, will be discussed in depth.
* **DeepSeek R1**, a fine-tuned version of Llama 70B using samples generated by DeepSeek R1, has demonstrated exceptional performance on tasks like MATH-500 (94.5%) and AIME 2024 (86.7%). It supports a full 128k context window and optimized token throughput, significantly enhancing its reasoning capabilities.

This meetup will delve into the methodologies behind fine-tuning, the technological advancements that support it, and the emerging evidence from models like Sonar and DeepSeek R1. Discussion will encompass techniques, infrastructure needs, and cost considerations involved in fine-tuning LLMs for reasoning tasks, revealing how businesses and researchers can leverage this practice to enhance performance and yield better results.

Join us as we explore how fine-tuning is challenging the traditional notion that base LLMs should not be fine-tuned, and how these optimized models are setting new benchmarks for efficiency and accuracy in AI search and reasoning.

**🎁 FounderCulture is powered by our Patrons** -- [consider becoming a Patron to support our vision](http://www.patreon.com/FounderCulture) of improving the odds for Founders globally *(you'll get early access to RSVP to our events!)*

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AI, AI Engineering, 2X startup founder, multiple successful exits, startup advisor, Ex-AWS

In my blog, I cover AI engineering topics extensively. LLM breakthroughs, engineering techniques, getting started, and attempts at predicting where the AI market is headed.

https://timkellogg.me/blog/

Checkout my series on getting started with AI Engineering:
https://timkellogg.me/blog/2024/12/19/ai-primer

Tim Kellogg

Enhanced base LLMs: Perplexity Sonar+DeepSeek R1 optimized by fine tuning Llama

San Mateo

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Building the Future of AI

Enhanced base LLMs: Perplexity Sonar+DeepSeek R1 optimized by fine tuning Llama

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