🚨BREAKING: LLaMA 3 Is HERE and SMASHES Benchmarks (Open-Source)

Summary

The video script discusses the launch of Llama 3, the latest model in the Llama series by Meta AI. The host expresses excitement about the new release, noting its significance in the world of AI and its potential to attract more people to artificial intelligence. Llama 3 is available in both 8 billion and 70 billion parameter versions, with the middle size version expected to follow. The model is positioned as a competitor to Chat GPT and is showcased for its impressive coding capabilities, including the quick creation of a Python snake game. The host also highlights the model's enhanced performance, ability to handle complex tasks, and its focus on agents as first-class citizens in AI. Additionally, the script touches on Meta AI's commitment to trust and safety with the release of Llama Guard 2 and other safety tools. The video concludes with the host's anticipation for further testing and integration of Llama 3 into various applications.

Takeaways

• 🚀 **Llama 3 Launch**: Meta AI has launched Llama 3, the third version of the Llama series, continuing the trend of open-source, locally run AI models.
• 🎨 **Tie-Dye for the Launch**: The speaker is excited about the launch, even wearing a tie-dye hoodie to celebrate the event.
• 📈 **Performance Enhancements**: Llama 3 offers enhanced performance with both 8 billion and 70 billion parameter versions, designed for a wide range of applications.
• 🔍 **Missing Middle Size**: There's an observation that the middle size version around 34 billion parameters is missing, implying potential future releases.
• 🤖 **AI Agents Emphasis**: Llama 3 positions AI agents as first-class citizens, highlighting their importance beyond simple prompts.
• 🐍 **Coding Test**: The speaker tests Llama 3's coding capabilities by asking it to write a Snake game in Python, which it does successfully and quickly.
• 📊 **Benchmarks and Scalability**: Llama 3 shows excellent performance in benchmarks, outperforming other models like Gemma 7B and MISTL 7B, especially in coding tasks.
• 🧩 **Multi-Step Task Capability**: The model's ability to handle multi-step tasks effortlessly is a significant improvement, beneficial for AI agents.
• 🔒 **Trust and Safety Updates**: Meta AI has updated its responsible use guide and trust and safety tools, including Llama Guard 2, to ensure responsible development and use of LLMs.
• 🌐 **Global Availability**: Meta AI is expanding its availability, rolling out in multiple countries and integrating into various platforms like Facebook, Instagram, and WhatsApp.
• 📱 **Mobile Integration**: Users can access Meta AI features through their mobile devices, making AI capabilities more accessible and convenient.
• 📸 **Image Generation**: Meta AI's image generation feature is now faster, allowing users to create images on the fly, with the option to animate them.

Q & A

What is the significance of the launch of Llama 3?

Llama 3 is the third version of the Llama series of models developed by Meta AI. It is significant because it continues the trend of open-source, locally run models that have helped a new generation of people get into artificial intelligence. It offers enhanced performance, scalability, and is capable of handling complex tasks like translation and dialogue generation with improved efficiency.

What are the different versions of Llama 3 that have been released?

Llama 3 has been released in both 8 billion and 70 billion pre-trained and instruction-tuned versions to support a wide range of applications. However, the middle size version around 34 billion parameters is missing, which is expected to be released in the future.

How does Llama 3 perform in coding tasks?

Llama 3 has shown significant improvements in coding tasks. It was able to generate a complete snake game in Python on the first try, which is a complex task. It also scored triple the math score of its competitors, indicating a strong capability for reasoning, code generation, and instruction following.

What are the trust and safety measures that Meta AI has implemented with Llama 3?

Meta AI has updated the Responsible Use Guide (RUG) and introduced Llama Guard 2, which includes tools like Code Shield and Cybersec SEC Eval 2. These tools are designed to ensure the models are used responsibly, looking for insecure code practices, susceptibility to prompt injection, and other potential issues.

How does Llama 3 compare to other models in terms of benchmarks?

Llama 3 outperforms its predecessor, Llama 2, across the board in benchmarks. When compared to other models like Gemma 7B and Mistil 7B Instruct, Llama 3's 8 billion parameter version showed superior results in MLU, GP QA, and human eval. The 70 billion parameter version also performed well against larger models like Gemini Pro 1.5 and CLA 3 Sonnet.

What is the context length supported by Llama 3?

Llama 3 supports an 8K context length, which doubles the capacity of Llama 2. While this is an improvement, it is still considered small compared to other models like GPT 4, which supports 128k, and Gemini Pro 1.5, which supports a million tokens.

How does Meta AI plan to integrate Llama 3 into their ecosystem?

Meta AI plans to integrate Llama 3 into their ecosystem through various applications and platforms like Facebook, Instagram, WhatsApp, and Messenger. It will be used for tasks such as recommending restaurants, finding events, and providing real-time information without leaving the app.

What are the potential use cases for Llama 3 in the AI stack?

Llama 3 can be used in various layers of the AI stack. At the infrastructure layer, it can be used for agent orchestration, evaluation, and deployment. At the app layer, it can be integrated into existing apps to add AI features or be the foundation for new AI-driven apps. It is expected to be particularly useful for developing agents and other AI-powered applications.

How does the release of Llama 3 impact the AI market?

The release of Llama 3 puts competitive pressure on closed models like GP4, Claude, and Gemini, potentially pushing down prices and commoditizing models. It signals a shift towards open-source models, which can democratize AI technology and make it more accessible to developers and users.

What are the trust and safety tools included in Llama Guard 2?

Llama Guard 2 includes tools like Code Shield, which protects against insecure code practices, and Cybersec SEC Eval 2, which evaluates the model for various security issues including susceptibility to prompt injection and offensive cybersecurity capabilities.

How can developers access Llama 3 models?

Developers can access Llama 3 models by visiting the Meta AI website and downloading the models. The models are available in both 8 billion and 70 billion parameter versions, and the code is open-sourced on GitHub, allowing developers to fine-tune the models as needed.

What is the significance of the 15 trillion tokens of data used to train Llama 3?

The 15 trillion tokens of data represent a significant increase in the training dataset size compared to Llama 2, which is seven times larger. This larger dataset, including four times more code, contributes to the enhanced capabilities and performance of Llama 3, making it one of the most capable models available.

Outlines

00:00

🚀 Introduction to Llama 3 and Meta AI's New Developments

The video script begins with excitement for the launch of Llama 3, the third version of the Llama series of models from Meta AI. The host talks about the impact of the original Llama leak on the open-source AI community and how it has influenced the field. The script introduces the new features of Llama 3, including its availability in both 8 billion and 70 billion pre-trained and instruction-tuned versions. The host also mentions the absence of a middle size version and highlights the model's capabilities for a wide range of applications. The script discusses the model's performance, particularly in multi-step tasks and complex scenarios like translation and dialogue generation. It also touches on the model's training on a vast dataset and its enhanced scalability and performance.

05:02

📊 Llama 3's Benchmarks and Trust & Safety Features

The second paragraph delves into the benchmarks of Llama 3, comparing its performance with other models like Google's Gemini Pro 1.5 and Mistil 7B. The host highlights Llama 3's significant lead in benchmarks, especially in math scores and code generation. The script also discusses Meta AI's focus on trust and safety, introducing the Responsible Use Guide (RUG) and the Llama Guard 2, which are designed to ensure the responsible development and use of the model. The host expresses enthusiasm for the model's potential in agents and AI-powered applications and anticipates further developments in this area.

10:04

🌐 Meta AI's Integration and Expansion of Llama 3

The third paragraph discusses the integration of Llama 3 into various applications and platforms, emphasizing its potential use in chat, search, and more across Meta's apps. The host suggests that Meta AI should integrate user context into Llama 3 to enhance its functionality. The script also mentions the improvements in Meta AI's image generation capabilities and its global rollout in several countries. The host describes the practical examples of how Meta AI can be used in everyday scenarios, such as planning a night out or a weekend getaway, and its incorporation into social media feeds and messenger apps.

15:05

📈 Llama 3's Performance and Future Testing

The final paragraph focuses on the performance of Llama 3, showcasing its improvements over Llama 2 across various benchmarks. The host expresses eagerness to test Llama 3 thoroughly using their evaluation rubric. The script also mentions the availability of Llama 3's code on GitHub, indicating that while the code is open-source, the original weights may not be. The host invites viewers to like, subscribe, and look forward to upcoming videos that will further explore Llama 3's capabilities.

Keywords

💡Llama 3

Llama 3 refers to the third version of the Llama series of AI models developed by Meta AI. It is a significant upgrade from its predecessors and is central to the video's theme as it represents the latest advancement in AI technology. The video discusses its features, capabilities, and potential applications, highlighting its importance in the field of artificial intelligence.

💡Meta AI

Meta AI is the company responsible for developing the Llama series of models. In the context of the video, Meta AI is portrayed as a pioneer in the open-source AI movement, having a significant impact on the accessibility and development of AI technologies. The video mentions Meta AI's role in releasing Llama 3 and its commitment to open-source contributions to the AI community.

💡Pre-trained and Instruction Tune Versions

These terms refer to the different configurations of the Llama 3 model. Pre-trained versions are models that have already been trained on a large dataset, while instruction-tune versions are further trained to follow specific instructions. These concepts are important as they highlight the flexibility and adaptability of Llama 3 for various applications, as mentioned in the video.

💡Multi-step Tasks

Multi-step tasks are complex problems that require multiple steps or stages to solve. In the context of the video, Llama 3's ability to handle such tasks effortlessly is emphasized, showcasing its advanced capabilities. This is particularly relevant for AI applications that require sequential reasoning and problem-solving, such as planning or decision-making processes.

💡Code Generation

Code generation is the process of creating source code automatically. In the video, it is highlighted as one of the key capabilities of Llama 3, with the model demonstrating impressive performance in coding tasks. This is significant as it suggests that Llama 3 can be effectively utilized in software development and programming, which are increasingly important in the tech industry.

💡Benchmarks

Benchmarks are standard tests or measurements used to assess the performance of a system or model. The video discusses Llama 3's benchmarks, comparing its performance with other models like Gemma 7B and Claude 3 Sonet. These benchmarks are crucial as they provide a quantitative measure of Llama 3's capabilities and effectiveness in various tasks.

💡Trust and Safety

Trust and safety refer to the ethical considerations and measures taken to ensure that AI technologies are used responsibly. The video mentions Meta AI's focus on trust and safety, highlighting the company's efforts to update its responsible use guide and introduce tools like Llama Guard 2. This is important as it addresses concerns about the potential misuse of AI and emphasizes the need for ethical AI development.

💡Llama Guard

Llama Guard is a system developed by Meta AI to ensure the safe and responsible use of their AI models. It is mentioned in the context of the video as part of Meta AI's trust and safety innovations. Llama Guard is significant as it represents the company's commitment to creating secure and trustworthy AI technologies.

💡Open Source

Open source refers to software or models whose source code is made available to the public, allowing anyone to view, use, modify, and distribute it. In the video, Meta AI's decision to open source the Llama 3 model is discussed, emphasizing the company's contribution to the AI community. This is significant as it promotes collaboration, innovation, and transparency in AI development.

💡Image Generation

Image generation is the process of creating images or visual content using AI. The video showcases Meta AI's advancements in image generation, where users can create and animate images through simple prompts. This feature is noteworthy as it demonstrates the versatility of AI models in creative applications and the potential for integrating AI into various forms of digital media.

💡AI Stack

The AI stack refers to the different layers of technology that make up an AI system, from hardware to applications. The video discusses the concept of the AI stack, suggesting that value in AI is shifting from the model layer to other layers such as infrastructure and applications. This is important as it reflects on the evolving landscape of AI technology and where future innovations and investments are likely to be focused.

Highlights

Llama 3, the third version of Meta AI's Llama series, has been launched.

Llama 3 is available in both 8 billion and 70 billion pre-trained and instruction-tuned versions.

The middle size version around 34 billion parameters is expected but not yet released.

Llama 3 is positioned as a competitor to Chat GPT with enhanced capabilities for agents.

Meta AI's new chat interface for Llama 3 allows users to test its capabilities directly.

Llama 3 demonstrated impressive performance by quickly generating a working Snake game in Python.

The model has been trained on a dataset seven times larger than Llama 2, including four times more code.

Llama 3 supports 8K context length, doubling the capacity of Llama 2.

Benchmarks show Llama 3 outperforming other models like Gemma 7B and Mistil 7B in various tasks.

Meta AI has released Llama Guard 2, enhancing trust and safety tools for responsible AI development.

Llama 3 integrates with Meta's apps for tasks like recommending restaurants and finding events.

Meta AI's image generation feature now produces images as you type, offering real-time creation.

The Llama 3 model is open source, allowing users to download and fine-tune the model.

Meta AI is expanding its availability globally, with support in over a dozen countries outside the US.

Llama 3 is being integrated into Meta's search and recommendation systems for a more personalized user experience.

The GitHub page for Llama 3 provides access to the model's code and benchmarks for developers.

Llama 3's training on 15 trillion tokens represents a significant advancement in AI model capabilities.

The speaker plans to conduct a full suite of tests on Llama 3 and integrate it into Crew AI.

Transcripts

llama 3 day is here what an exciting day

I even broke out the tie-dye hoodie just

for this launch today we are going to be

talking all about llama 3 we're going to

review the announcement I'm going to

show you what's new about it what's

different about it and I have a bunch of

videos planned for llama 3 including

testing and coding and fine-tuning

everything so very exciting times if

you're not already subscribed be sure to

subscribe to continue getting awesome AI

content so let's dig into it so just a

few minutes ago we had the launch of

llama 3 this is the third version of the

Llama series of models out of meta Ai

and taking a step back the original

llama leak which was about a year ago

was really what set off the entire

open-source locally run model craze and

I am so thankful to meta and whoever

leaked it because it got an entire new

generation of people into AR icial

intelligence and then we had llama 2

which was a huge upgrade from llama 1

and now today we have llama 3 so let me

show you what it's all about this is the

blog post build the future of AI with

meta llama 3 you can find this you can

download the models and everything from

here l.a. comom lama3 now available with

both 8 billion and 70 billion

pre-trained and instruction tune

versions to support a wide range of

applications now if you're a keen

Observer you're probably noticing

they're missing that middle size version

around 34 billion parameters I'm sure

it's coming and it looks like meta AI

has released or maybe they hadn't at I

didn't know about it essentially a chat

GPT UI competitor right here so we can

actually test it out and I'll run a

quick couple tests on it just to show

you but the full battery of tests I'm

going to save for another video and it's

interesting that they say right here

whether you're developing agents or

other AI powered applications these

models offer the capabilities and

flexibility you need to develop your

ideas and basically agents are now first

class citizens in the world of AI there

were a lot of people who doubted that

agents are actually a thing because they

said well it's just a prompt right it's

so much more than that and I'm glad that

meta is seeing that and also knows that

and here it is meta AI meta aai this is

their new chat interface for llama 3

Let's just test something really quickly

and I bet you know what I'm going to

test write the Game snake in Python all

right here we go it is lightning fast

look at this and I'm not actually sure

if it's using the 8 billion or 70

billion parameter version but it is

super fast so we're going to copy the

code and it is using the curses Library

so I think that means it's going to be

terminal based all right pasted the code

in here doesn't look like we have any

immediate errors let's push play all

right we have a working snake game in

fact this is one of the more complete

games that I've seen amazing it even

gives me the score it has a window and

this time the snake snake can go through

which is cool and let's see what happens

if the snake goes into itself if I can

actually do that yep and it crashes

Flawless this would be an absolute pass

for llama 3 so again be sure to check

out my coming videos where I'm going to

do the full Suite of tests and if you

want to try it yourself just go to meta

and now they have their own inference

front end which is amazing all right

let's keep reading so we have enhanced

performance enhanced stated thee art

performance of llama 3 and openly

accessible model that excels at language

nuances contextual understanding and

complex tasks like translation and

dialogue generation with enhanced

scalability and performance llama 3 can

handle multi-step tasks effortlessly

while our refined posttraining processes

significantly lowers false refusal rates

improve response alignment and boost

diversity and model answers so this is

all good I'm especially interested in

multi-step tasks being handled

effortlessly because that in my mind

screams a agents and you know I'm going

to be plugging this into crew AI

additionally it drastically elevates

capabilities like reasoning code

generation and instruction following and

they have the download model link right

here if we click it you request access

put in your information why would we

ever want to get llama 2 anymore meta

code llama I can't wait for meta code

llama to be based on llama 3 and then

you download it and here are the

benchmarks so llama 3 models take data

and scale to new heights it's been

trained on our two recently announced

custombuilt 24,000 GPU clusters on over

15 trillion tokens of data a training

data set seven times larger than that

used for llama 2 including four times

more code I love the coding use case I'm

so glad that they're using a lot more

code obviously it's really good I just

tested it with snake and it got it on

the first try this results in the most

capable llama model yet which supports

8K context length and that doubles the

capacity of llama 2 8K is still pretty

small GPT 4 is 128k and even that's

small nowadays Gemini Pro 1.5 is a

million tokens so we're getting a bit

jaded with our token limits now uh AK is

small but fine-tuned versions will

increase that drastically and hopefully

it still maintains the quality looking

at the benchmarks they are comparing the

8 billion parameter version to Gemma 7B

which is Google's small open model and

mistl 7B instruct which is always one of

my favorites if not my favorite but now

metal Lama 38b the clear winner the

clear winner for these smaller models

for the mlu five shot 78.4 compared to

53 and 58 GP QA zero shot 34 compared to

21 and 26 basically across the board

human eval GSM AK math I mean look at

the math score for llama 3 the math

score is triple what Gemma 7B and mistl

7B instruct are so I should probably add

more comp complex math questions at this

point to my llm rubric if you have any

that you suggest drop it in the comments

below I'll add it then we have the large

model and here's the interesting thing

meta decided to compare their large 70b

model against Gemini Pro 1.5 which is

Clos source that is Google's

top-of-the-line model the million token

context window model and CLA 3 Sonet but

not Cloud 3 Opus and Cloud 3 Opus is

pretty much regarded as the best model

out there Clos open doesn't matter it is

the best so it's interesting that they

only compared it against clae 3 Sonet

because the Sonet model is the middle

model of all three of the Claude models

so again MML five shot it won but just

barely GPT QA it lost but just barely

human eval it is much better and as a

reminder human eval is code generation

so look at this llama 38b code

generation double that of Gemma 7B and

mistl 7B and for llama 370b human eval

is 81 nearly 82 Gemini Pro 71 and CLA 3

Sonic 73 so they really went all out on

the coding aspect of this model and

that's my favorite use case so I could

not be more excited that probably also

means it's really good at function

calling and so again I think agents GSM

8K at one and then for the math

benchmark it got a 50 which is actually

a bit less than Gemini Pro 1.5 and quite

a bit bit more than Cloud 3 Sonet and

we're going to talk a little bit about

trust and safety because that is a big

theme for meta especially because

they're open sourcing all of this they

really want to make sure that people are

using it responsibly whatever that

actually means and so let's take a look

at some of their new Innovations for the

trust and safety category so with the

release of llama 3 we've updated the

responsible use guide rug to provide the

most comprehensive information on

responsible development with llms our

system Centric approach includes updates

to our trust and safety tools with llard

2 so I've not actually heard of lard

surprisingly optimized to support the

newly announced taxonomy published by ml

Commons expanding its coverage to a more

comprehensive set of safety categories

code shield and cyers SEC eval 2 so what

is llama guard making safety tools

accessible to everybody so enabling

developers advanced Safety and building

an open ecosystem so llama guard is

their kind of architecture for making

sure that the models are being used

appropriately and here's what it looks

like we have the responsible llm product

development stages determine the use

case we have the model level so you're

actually creating the model we have the

system level where llama guard 2 and

llama code Shield are being implemented

and then building transparency here is

an evaluation meet llama cyber secc eval

and basically what it does is looks for

insecure code practices cyber attacker

helpfulness code interpreter abuse

offensive cyber security capabilities

and susceptibility to prompt injection

so it'll be interesting for this last

one because there have been a number of

jailbreaking techniques that have just

worked and completely shattered The

Cutting Edge closed source and open

source models and they have an entire

paper for this but I'm not going to go

over that now if you want to see that

let me know in the comments below and

yeah meta AI is brand new that's meta

doai that is their front end to their

inference engine they are basically

competing with chat GPT but it is free

at least for now so a better assistant

thanks to our latest advanced with metal

Lama 3 We Believe meta AI is now the

most intelligent AI system you can use

for free boy their continued releases

into the open source Community is such a

great defensive competitive play I

really believe that and the more they

release for free the more pressure they

put on closed models like gp4 like

Claude like Gemini and they will

continue to push down the price which is

good for everybody else for us

developers and users of these systems

and I've also been saying that models

are becoming commoditized very quickly

and we talked in a previous video a lot

about the AI stack and where the value

is going to be right so at the bottom we

have the hardware layer that's the

Invidia and the grocs of the world and

there's going to be a lot of value

created there but that you know probably

had to be started many years ago above

that we have the infrastructure layer

and that's agent orchestration tools and

evaluation tools and observability

deployment everything like that there's

going to be a ton of value there we have

the model layer which I don't think

there's going to be a lot of value in

the long run there and then at the very

top we have the app layer so apps built

with or apps completely built on top of

AI or existing apps that are now having

AI features and I do think that there's

going to be a lot of value there

although we haven't seen that yet so you

can use meta AI in feed chats search and

more across our apps to get things done

and access real-time information without

having to leave the app now what I think

they need to do and I don't think

they've done it yet but I bet they will

is start to integrate all the context

that you already have as a user of all

their systems into llama 3 so it

shouldn't just be a stateless engine all

the time we should be able to ask

questions about whatever's on the page

about my chat history about things that

I've done in the past that's really what

I'm excited about and now meta ai's

image generation is now faster producing

images as you type so you can create

album artwork for your band Decor

inspiration great I didn't actually know

you can create images with meta AI so

let me just give that a try real quick

create me an image of a robotic llama

all right yep there it is very cool

let's see what it looks like if it's any

good so I typically use Dolly and I'm

really happy with it it's fine wow okay

this is really good it's definitely not

as high quality as Dolly but it's good

and if you look right here we have an

imagined Watermark so they do Watermark

all all of their AI images so yep you

can use meta AI it's already on your

phone in your pocket for free starting

to go Global with more features you can

use it on Facebook Instagram WhatsApp

and messenger boy they are going hard to

get things done learn create and connect

with the things that matter for you they

are rolling out meta AI in English in

more than a dozen countries outside of

the US now people have access to meta AI

in Australia Canada Ghana Jamaica Malawi

New Zealand Nigeria Pakistan Singapore

South Africa Uganda Zambia and Zimbabwe

and we just getting started so this is

an example planning a night out with

friends ask meta AI to recommend a

restaurant with sunset views and vegan

options so it looks like it already does

have external context oh my God this is

so exciting organizing a weekend getaway

ask meta AI to find concerts for

Saturday night and we can just watch the

example happening right here so this is

within the messenger app you ask it a

question and boom pops it up right there

the information you need so you just at

meta AI it and then you can ask it

questions very very cool and they're

also including llama 3 meta AI in search

as well this is a huge launch for meta

so show me a video of the recipe great

these are really cool examples oh wow

they're even putting it in your feed so

I'm not a big Facebook user in fact I

don't really use Facebook at all but now

meta AI is available directly in your

feed that is their bread and butter

product that is how they make so much

money so it's interesting to see how

completely invested in Ai and

specifically llama 3 meta is so it's a

really good signal that if you're a

developer and you're thinking about

where to build your AI app llama 3 is

probably a pretty good option and here's

another example so you can simply say

imagine a bird and it'll give you an

image of a bird and then you say animate

and it will actually animate it so

that's very very cool now I still think

that the quality of the images is not

quite at the dolly or the mid journey

level but that's okay it's completely

fre which is nice and then yeah meta

just saying animate turns it into a gift

that you can share now here is the meta

llama GitHub page so they do actually

have the code and here it is here's the

code for llama 3 you could download the

models so I don't believe this is open

weight at least not yet now maybe I'm

getting that wrong but I don't see

anywhere where it says open weight so

this is open source because they are

open sourcing the code and you can

download the model and do with it what

you like you can fine-tune it but I

don't think the original weights are

released so if you want to check out the

GitHub repository it's github.com sleta

l/ lama3 and here's the model card for

it and this will likely be available on

hugging face if it's not already and

there's that 15 trillion token count

which is just insane that is an insane

amount of tokens and then they boiled it

all down to effectively the same size

model as lamao so it's 8B instead of 7B

and 70 billion parameter models and here

are a bunch of benchmarks here's llama 2

7B llama 2 13B and a bunch of benchmarks

and then llama 38b and across the board

100% of their scores are beating llama 2

and then here's the llama 370b and you

can see it is a market improvement from

the Llama 270b model again across the

board so I'm super excited to test this

out as soon as I'm done with this video

I'm going to start recording another

video testing it putting it through its

Paces in my llm rubric so if you enjoyed

this video please consider giving a like

And subscribe and I'll see you in the

next one