Hello Learners…
Welcome to the blog…
Table Of Contents
- Introduction
- New Short Course : Boosting LLM Application Accuracy Through Deep Learning Techniques
- Building a Text-to-SQL Agent
- Developing Evaluation Metrics
- Employing Advanced Fine-Tuning Techniques
- Hands-On Experience with Llama Models
- Summary
- References
Introduction
In the post, New Short Course : Boosting LLM Application Accuracy Through Deep Learning Techniques we overview a new course by deeplearning.ai to enhance the accuracy of LLM applications.
Developers often encounter challenges when working with Large Language Model (LLM) applications, particularly when it comes to achieving consistent and accurate results.
To address these issues, Deeplearning.ai has designed a comprehensive short course aimed at enhancing the accuracy and reliability of LLM applications.
This course equips developers with practical tools and techniques to systematically improve LLM performance, from building an SQL agent to fine-tuning models with advanced methods.
New Short Course : Boosting LLM Application Accuracy Through Deep Learning Techniques
Many developers have experienced frustration with inconsistent results when working with LLM applications.
This course offers a systematic approach to enhance the accuracy and reliability of our LLM applications.
We will build an SQL agent, add evaluation metrics to measure performance, and use prompt engineering and self-reflection to make the model perform better.
Finally, we will fine-tune the model with techniques like LoRA and memory tuning that embeds facts in model weights to reduce hallucinations.
In this course, we will use Llama’s family of open-source models.
What we will do:
Building a Text-to-SQL Agent
- Participants will begin by constructing a text-to-SQL agent, learning how to simulate scenarios where the model may hallucinate.
- This sets the foundation for understanding the importance of evaluating and improving LLM performance.
Developing Evaluation Metrics
- The course covers the development of a systematic evaluation framework, teaching participants how to create criteria for good evaluations, establish best practices, and develop a comprehensive evaluation score.
- This ensures that the LLM applications are consistently delivering accurate results.
Employing Advanced Fine-Tuning Techniques
- Participants will explore advanced fine-tuning methods such as Low-Rank Adaptation (LoRA) and memory tuning.
- These techniques are crucial for embedding facts into model weights, thereby reducing hallucinations and enhancing the model’s ability to follow instructions accurately.
Hands-On Experience with Llama Models
- The course offers practical, hands-on experience with the Llama family of open-source models.
- This allows developers to apply the skills they have learned in real-world scenarios, ultimately elevating their LLM applications to a higher level of performance and reliability.
Start improving the accuracy of LLM applications today!
Summary
This course provides a robust framework for developers looking to improve the accuracy of their LLM applications.
By guiding participants through the process of building a text-to-SQL agent, developing evaluation metrics, and employing advanced fine-tuning techniques like LoRA and memory tuning, the course ensures that models are better equipped to handle real-world tasks with reduced hallucinations. Through hands-on experience with the Llama family of open-source models, developers will gain valuable skills to elevate their LLM applications to the next level.