Introduction to large language models

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Large Language Models (LLMs) and Generative AI intersect and they are both part of deep learning. Watch this video to learn about LLMs, including use cases, Prompt Tuning, and GenAI development tools.

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Appreciate the valuable content! Sharing some key takeaways of the video and I hope this can help someone out.

1) 00:50 - Large language models (LLMs) are general purpose language models that can be pre-trained and fine-tuned for specific purposes.

LLMs are trained for general purposes to solve common language problems, and then tailored to solve specific problems in different fields.

2) 02:04 - Large language models have enormous size and parameter count.

The size of the training data set can be at the petabyte scale, and the parameter count refers to the memories and knowledge learned by the machine during training.

3) 03:01 - Pre-training and fine-tuning are key steps in developing large language models.

Pre-training involves training a large language model for general purposes with a large data set, while fine-tuning involves training the model for specific aims with a much smaller data set.

4) 03:15 - Large language models offer several benefits.

They can be used for different tasks, require minimal field training data, and their performance improves with more data and parameters.

5) 08:50 - Prompt design and prompt engineering are important in large language models.

Prompt design involves creating a clear, concise, and informative prompt for the desired task, while prompt engineering focuses on improving performance.

6) 13:43 - Generative AI Studio and Generative AI App Builder are tools for exploring and customizing generative AI models.

Generative AI Studio provides pre-trained models, tools for fine-tuning and deploying models, and a community forum for collaboration.

7) 14:52 - Palm API and Vertex AI provide tools for testing, tuning, and deploying large language models.

Palm API allows testing and experimenting with large language models and gen AI tools, while Vertex AI offers task-specific Foundation models and parameter efficient tuning methods.

genlu

The mere fact that every large player in this space has videos teaching people about these things means this is super super serious.

EKOLegend

Fantastic presentation...and...(I LOVE THIS) NO ANNOYING BACKING TRACK!! Thank you, Google!

davidcottrell

Minor Correction @ 2:14. "In ML, parameters are often called hyperparameters." In ML, parameters and hyperparameters can exist simultaneously and serve two different purposes. One can think of hyperparameters as the set of knobs that the designer has direct influence to change as they see fit (whether algorithmically or manually). As for the parameters of a model, one can think of it as the set of knobs that are learned directly from the data. For hyperparameters, you specify them prior to the training step; while the training step proceeds, the parameters of the model are being learned.

dariannwankwo

Actually, really helpful, thank you Google.

Wondering how far this technology will go in the next couple of years, if it's this far already in a couple of months.

yabadab

Finding answers to questions has become so much easier now with new tech. I have never been good at writing code, so this is a welcome change as far as I'm concerned! Look forward to more progress in technology.

joseperez-igyu

Thank you for making this available to the general public!

sarahsalt

Can't wait to see demos at GoogleIO

JonathanPoczatek

This is one of the educative sessions I've come across

fred-nyanokwi

Thank you John. I believe you conflated model parameters and hyperparameters at 2:16. As far as I know, these are two different concepts.

henri

This was fantastic! While I've been watching The Full Stack LLM Bootcamp, I'm not technically strong enough to start there, and will use these Google Cloud Tech videos as a means to "jumpstart" my knowledge of LLM and Generative AI. This is a great general primer for students and colleagues!

robertcormia

It's very clear to understand LLM, thank you

jamesmina

Thank you. I understood about half (optimistically) of it. I subscribed to the channel hoping to start from the beginning and understanding more. My ultimate goal: a LLM Librarian, combining the catalog of a library with results from internet search engine, giving the deepest answer possible.

richardglady

proximity and stream for seek time reduction..memory in case reduced latency, can also be optimized for seek time and pattern analysis.

MontEtteineneye

2:47 You mentioned the parameters are hyper parameters is incorrect and confusing

BrandonLee-ikkw

Great explainer. I'm a little less anxious about AI taking our jobs.

bakerkawesa

If you define the problem you are trying to solve first

Then reason from their

Wouldn’t it be more efficient?

luminouswolf

Very comprehensive video! Thank you guys!

cassianocominetti

Very Informative - Thanks for sharing 😊 prompt design and prompt engineering would take make the conversation more realistic and accurate.

jeganathanmanickam

Wow!
Thank you for this very useful video so well explained!

higiniofuentes

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