Beat GPT-4 with a Small Model and 10 Rows of Data and Synthetic Data Generation

While fine-tuning small language models with high quality datasets can consistently yield results that rival large foundation models like GPT-4, assembling sufficient fine-tuning training data is a barrier for many teams.

This webinar introduces a novel approach that could change that paradigm. By leveraging large language models like GPT-4 and Llama-3.1-405b to generate synthetic data, we explore how teams can achieve GPT-4 level results with as few as 10 real-world examples. Join us to learn about this emerging technique and its implications for fine-tuning small language models (SLMs).

In this webinar, we cover:
- The persistent challenge of insufficient training data in AI development
- Techniques for generating high-quality synthetic data using Llama-3.1-405B & GPT-4
- How to achieve GPT-4 level performance with small, fine-tuned models
- Ways to significantly reduce data collection efforts and get to production faster

Discover how this approach could streamline your AI development process and open new possibilities with small language models.

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Can we use phi-3 3B or gemini 2B for fine tuning custom data.

Given a Job description extract technical skills only from it.