What If Your LLM Could Become an Expert on Anything You Want? 

Great video, thank you Maya! Can you do a video on other use cases for fine-tuning LLMs and provide some examples?

I'd like to get a deeper understanding of this topic. Can you please suggest any materials for this?

@@alexdw5 Thanks for the feedback! That's a great idea and I think It might be the topic of my next video!

@@maya-akim oh awesome! Your videos rule! I’ve subscribed and will keep an eye out for it

@@getlucky8952 I just updated description box with the resources that I've been using for this video, so you can check it out! One more thing that I can recommend is perplexity.ai It does all the research for you and provides links to sources. It's very easy to research new topics this way. It's GPT4 + access to internet

Thanks a lot 🙏🏻

She is running it in the cloud, but you can also run it in a much more user-friendly manner using the text-generation GUI. However, this approach requires a substantial amount of RAM to operate. But don't worry, the investment in resources is worthwhile. By utilizing this method, you can train your own AI model that has the potential to outperform even GPT-4 in terms of intelligence. The catch is that this AI model can achieve excellence in only one particular area, though not necessarily a comprehensive mastery across diverse tasks like GPT-4 does with its trillion parameters. Consider the analogy of specialized tools versus a Swiss Army knife. The cloud-based approach with a text-generation GUI is akin to a specialized tool that is tailored for a specific task. For instance, think of a precision instrument designed solely for crafting intricate woodwork. It excels in this task but wouldn't be ideal for opening a bottle, unlike a Swiss Army knife that can handle various tasks reasonably well. Imagine you wish to develop AI for medical diagnosis and language translation. Instead of aiming for an all-knowing AI on a consumer-grade PC, you could train one AI model meticulously for medical diagnostics and another for language translation. This focused training allows each AI to excel in its dedicated field, just like having a medical specialist and a linguistic expert. In essence, the principle here is specialization. If you want an AI that is outstanding at a specific task, it's best to concentrate its training on that area, much like how an Olympic athlete dedicates themselves to excelling in one sport. This doesn't mean your AI won't be intelligent or useful; it means it will be exceptionally proficient in its chosen field. In conclusion, the prospect of training specialized AI models, each dedicated to excelling in a particular task, is akin to cultivating experts in various fields rather than seeking an all-encompassing omniscient AI. This strategy optimizes resource usage and results in AI systems that are highly competent in their designated domains.

Check the page "Baby Step: Fine-Tune Falcon-7B and LLaMa-7b Large Language Models for your custom datasets." It shows how to train and then load for inference.

Very well said!

Settings, how do they work?