Master LLMs: Top Strategies to Evaluate LLM Performance
practical tools to select the right model for your specific needs and tasks
Good morning fellow AI enthusiast! This is the fourth iteration of my video series for our free course "Training & Fine-Tuning LLMs for Production"!
In this one, we look into how to evaluate and benchmark Large Language Models (LLMs) effectively.
Learn about perplexity, evaluation metrics, and curated benchmarks to compare LLM performance with practical tools and resources to select the right model for your specific needs and tasks.
I also asked Plato what he thinks about LLMs and how to compare them...
1️⃣ Master LLMs: Top Strategies to Evaluate LLM Performance
There are hundreds of large language models (LLMs) available for you to use: From closed-source LLMs like GPT4 and Claude, open-source LLMs like Llama 2 and Falcon, or even your own fine-tuned LLM as we teach in our course.
But how do you ensure you are using the best model you can for the task you want?
After this video, you will know how to choose the right LLM for your use case and what to look for if you decide to fine-tune an LLM yourself!
P.S. Quick tip: If you are time and resource-limited, the best choice is to use closed-source models like GPT4 or Claude. The teams behind them make sure to give you the best performance they can out of those models from the get go. Still, you may want to evaluate and compare their performances, especially when your application becomes more and more specific and you would like to switch to your own LLM.
Learn more in the video:
2️⃣ More about our course in collaboration with Towards AI, Activeloop, and the Intel Corporation disruptor initiative!
Tl;dr: The course is about showing everything about LLMs (train, fine-tune, use RAG…), and it is completely free!
Is the course for you?
If you want to learn how to train and fine-tune LLMs from scratch, and have intermediate Python knowledge, you should be all set to take and complete the course.
This course is designed with a wide audience in mind, including beginners in AI, current machine learning engineers, students, and professionals considering a career transition to AI.
We aimed to provide you with the necessary tools to apply and tailor Large Language Models across a wide range of industries to make AI more accessible and practical.
3️⃣ What does Plato think about this video?
I asked Plato to share his thoughts on this video... Here's what he had to say:
Behold the wonders of your era! In my time, knowledge was sought through dialogue and dialectic. Now, 'LLMs' produce knowledge, reminiscent of the Oracle's prophecies. You evaluate these 'models' based on 'perplexity,' much like we tested arguments in the Academy. However, true wisdom surpasses mere mimicry.
These LLM tasks, from coding to reasoning, blur the lines between shadows and true forms. The benchmarks are reminiscent of our philosophical inquiries at the Academy. The 'TruthfulQA' reveals an eternal quest for truth, even in this technologically advanced age.
While marveling at your advancements, remember Socrates' wisdom: the unexamined life is not worth living. Let your course guide others to meld technology with true understanding (and evaluate your AI models!).
And that's it for this iteration! I'm incredibly grateful that the What's AI newsletter is now read by over 13,000+ incredible human beings and counting. Share this iteration with a friend if you learned something new!
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Thank you for reading, and we wish you a fantastic week! Be sure to have enough rest and sleep!
Louis