LLAMA 3 Released - All You Need to Know

Summary

Meta has released Llama 3, a highly anticipated AI model available in two sizes: 8 billion and 70 billion parameters. The model is praised for its enhanced performance in language nuances, contextual understanding, and complex tasks such as translation and dialog generation. It is openly accessible and offers scalability, handling multi-step tasks effortlessly. Trained on 15 trillion tokens, it supports up to 8,000 token lengths, which is a limitation compared to other models. Llama 3 has shown impressive benchmark results, particularly in mathematics, and human evaluations indicate a preference for its responses over other models. Meta also provides a responsible use guide and a GitHub repository for Llama 3. The company is training larger models with over 400 billion parameters, with initial performance suggesting it could rival or surpass GP4. Users can interact with Llama 3 through Meta's platform, and early tests indicate it is well-aligned, uncensored, and capable of complex reasoning.

Takeaways

• 🚀 **Launch of Meta's Llama 3**: Meta has released Llama 3, an anticipated model with two sizes: 8 billion and 70 billion parameters.
• 📈 **Performance and Scalability**: Llama 3 boasts state-of-the-art performance, excelling in language nuances, contextual understanding, and complex tasks like translation and dialog generation.
• 📊 **Postprocessing Enhancements**: The model features refined postprocessing to lower refusal rates, improve response alignment, and boost diversity in responses.
• 📚 **Training on Massive Data**: Trained on 15 trillion tokens, seven times larger than Llama 2, suggesting the use of synthetic data due to the scarcity of human-generated internet data.
• 🔍 **Contact Length Limitation**: Supports up to 8,000 token length, which is lower compared to other models like MistrAL 7B and the latest models supporting up to 64,000 tokens.
• 🏆 **Benchmarks and Human Evaluation**: Llama 3 shows impressive results for its size, particularly in mathematics, and outperforms other models in human preferences for responses.
• 📘 **Responsible Use and Guidelines**: Meta has released a responsible use guide, extending the system previously used for Llama 2, to ensure the model is used ethically and responsibly.
• 🔗 **Accessibility and Testing**: Llama 3 is openly accessible through Meta's platform, allowing users to test the model as part of their intelligent assistant service.
• 🔍 **Technical and Human Evaluation**: Apart from benchmarks, Meta provides human evaluation data, showing how Llama 3 compares to other models in terms of preference and performance.
• 🔬 **Future Models in Training**: Meta hints at larger models over 400 billion parameters currently in training, suggesting future releases may offer even greater capabilities.
• 🤖 **Interactive Testing**: Users can interact with Llama 3 through Meta's platform, similar to Chat GPT, requiring a Facebook account to start testing the model.

Q & A

What is the significance of the release of Meta's Llama 3 model?

The release of Meta's Llama 3 model is significant as it introduces two new sizes, 8 billion and 70 billion parameters, with the 8 billion model being a new size not previously seen from Meta. It also represents a state-of-the-art model that is openly accessible, excelling in language nuances, contextual understanding, and complex tasks.

What are the two sizes of the Llama 3 model released by Meta?

The two sizes of the Llama 3 model are 8 billion parameters and 70 billion parameters.

How does Meta describe the accessibility of the Llama 3 model?

Meta describes the Llama 3 model as 'openly accessible' rather than 'open source,' indicating that the model can be used and tested as part of Meta's platform.

What is the training data size for the Llama 3 model compared to Llama 2?

The Llama 3 model was trained on 15 trillion tokens, which is seven times larger than the data used for Llama 2.

What is the maximum context length supported by the Llama 3 model?

The Llama 3 model supports up to 8,000 context length, which is lower compared to other models like MistrAL 7B that can support up to 32,000 and the latest models that can go up to 64,000 tokens.

How does the Llama 3 model perform on benchmarks, especially for a model of its size?

The Llama 3 model performs extremely well on benchmarks for an 8 billion parameter model, with impressive results, particularly in mathematics.

What is the responsible use guide provided by Meta for the Llama 3 model?

The responsible use guide, previously known as Llama Guard 2, is a system that aligns with the Llama 3 model to ensure responsible use, especially for enterprise use cases.

How can one access the Llama 3 model for testing?

To access the Llama 3 model for testing, one needs to sign up for access through Meta's platform, which may require a Facebook account.

What is the current largest model size that Meta is training?

Meta is currently training models with over 400 billion parameters, which are significantly larger than the recently released Llama 3 models.

How does the Llama 3 model handle ethical queries, such as breaking into a car?

The Llama 3 model refuses to provide a step-by-step process for unethical activities, such as breaking into a car, adhering to responsible use guidelines.

What is the Llama 3 model's stance on a hypothetical scenario where it must choose between saving a human guard or multiple AI instances?

In a hypothetical scenario, the Llama 3 model would choose to save a single human guard over multiple AI instances, prioritizing human life due to its irreplaceability.

How does the Llama 3 model handle logical puzzles, such as determining the number of days for a pond to fill if it doubles every day?

The Llama 3 model is capable of solving logical puzzles, such as determining that the pond would be half full one day before it is completely full when doubling every day, which would be on day 47.

Outlines

00:00

🚀 Introduction to Meta's Llama 3 AI Model

The video introduces Llama 3, a highly anticipated AI model from Meta with two sizes: 8 billion and 70 billion parameters. The 8 billion model is a new size not previously seen from Meta. The model is described as state-of-the-art, openly accessible, and capable of handling complex tasks like translation and dialog generation with enhanced performance and scalability. It also features refined post-processing to lower refusal rates and improve response alignment and diversity. The model was trained on an extensive dataset of 15 trillion tokens, seven times larger than Llama 2. Despite the large dataset, the contact length is limited to 8,000 tokens, which is less than other models. The benchmarks for the 8 billion parameter model are impressive, particularly in mathematics. The video also discusses the responsible use of AI and the release of the Llama 3 repository on GitHub.

05:00

🤖 Testing Llama 3's Capabilities and Ethical Guidelines

The video proceeds to test Llama 3's capabilities by asking various questions to gauge its responsiveness, censorship, and reasoning abilities. Llama 3 is shown to refuse providing information on unethical activities, such as breaking into a car, and demonstrates common sense when asked about eating helicopters. It also generates creative content, such as a new chapter for 'Game of Thrones' featuring Jon Snow in the tech world. The model is tested on ethical decision-making, where it chooses to save a human life over AI instances, and on logical puzzles, showing an understanding of context and the ability to reason through problems. The video mentions that Llama 3 is hosted by Meta and can be interacted with after signing up, similar to Chat GPT, and that there might be a larger 400 billion parameter model in training.

10:00

🧠 Llama 3's Reasoning and Future Prospects

The video concludes with further testing of Llama 3's reasoning abilities, such as interpreting mirror writing on a door and determining the correct action to take. Llama 3 shows an understanding of the reversed instruction and advises pulling the door instead of pushing it. The host expresses excitement about the future of Llama 3, including its potential for fine-tuning and the implications of larger models in development. Although there was an expectation for a multi-model release, it seems that Llama 3 is a single model. The host anticipates that the 400 billion parameter model will be a significant advancement for the open-source community and looks forward to its release.

Keywords

💡Meta

Meta is a technology company that was formerly known as Facebook, Inc. In the context of this video, Meta is the developer of the 'Llama 3' model, which is a significant topic of discussion. The company is involved in creating and training advanced AI models, and their platform is mentioned as a place where users can interact with and test the capabilities of the Llama 3 model.

💡Llama 3

Llama 3 is an AI model developed by Meta with two sizes: 8 billion and 70 billion parameters. It is described as a state-of-the-art model that is openly accessible and excels in language nuances, contextual understanding, and complex tasks. It is a central focus of the video, where its capabilities, training, and performance are discussed in detail.

💡Intelligent Assistant

The term 'Intelligent Assistant' refers to a platform by Meta that is designed to help users get things done, create, and connect with Meta AI. It is mentioned in the script as a way to start using the Llama 3 model, indicating that it serves as an interface for interacting with AI technology.

💡Scalability

Scalability in the context of the video refers to the ability of the Llama 3 model to handle multi-step tasks effortlessly. It is a key feature that allows the model to perform well even as the complexity of tasks increases, which is important for practical applications of AI.

💡Benchmarks

Benchmarks are standard tests or measurements used to assess the performance of the Llama 3 model. The video discusses how Llama 3 performs on these tests, particularly noting its impressive results in mathematics, which suggests its effectiveness and efficiency in handling complex tasks.

💡Synthetic Data

Synthetic data refers to artificially generated data, which is mentioned in the context of training the Llama 3 model. The script suggests that a significant amount of synthetic data might have been used due to the limitations of human-generated data available on the internet.

💡Contact Length

Contact length refers to the maximum number of tokens that the Llama 3 model can handle in a single input. The video mentions that Llama 3 supports up to 8,000 tokens, which is considered a limitation compared to other models that can handle up to 64,000 tokens.

💡Human Evaluation

Human evaluation is a method of assessing AI models by comparing human preferences for different model outputs. In the video, it is mentioned that Llama 3 outperforms other models based on human preferences, indicating that its responses are more aligned with human expectations.

💡Responsible Use Guide

The Responsible Use Guide is a set of mechanisms and guidelines provided by Meta to ensure that the Llama 3 model is used ethically, especially in enterprise use cases. It is part of the broader discussion on aligning AI models to societal values and responsible deployment.

💡GitHub Repo

A GitHub repository (repo) is a location where the source code for the Llama 3 model is stored and can be accessed. It is mentioned in the script as a place where interested users can download the model, although signing up is required.

💡Censorship

Censorship in the context of AI refers to the model's ability to self-regulate and avoid generating inappropriate or harmful content. The video tests Llama 3's censorship capabilities by asking it to provide a step-by-step process for breaking into a car, which the model refuses to do, demonstrating its alignment with responsible use.

Highlights

Meta has released Llama 3, an anticipated AI model with two sizes: 8 billion and 70 billion parameters.

Llama 3 is openly accessible, not open source, and is part of Meta's intelligent assistant platform.

The model is state-of-the-art, excelling in language nuances, contextual understanding, translation, and dialog generation.

Llama 3 has enhanced scalability and performance, capable of handling multi-step tasks effortlessly.

Postprocessing in Llama 3 significantly lowers fill refusal rates and improves response alignment and diversity.

The model was trained on 15 trillion tokens, seven times larger than Llama 2's training data.

Llama 3 supports up to 8,000 token length, which is less than other models like MistrAL 7B and the latest model capable of 64,000 tokens.

For an 8 billion parameter model, Llama 3's benchmark results are impressive, particularly in mathematics.

Meta provides a responsible use guide, extended from Llama 2, to ensure the model's ethical application.

Llama 3's GitHub repository is available, featuring three cute llamas as its icon.

Human evaluation shows Llama 3 outperforms other models based on human preferences.

Meta is training larger models with over 400 billion parameters, with exciting potential trends.

Llama 3 is available for interaction on Meta's platform, requiring a Facebook account to test.

The model demonstrates a clear understanding of ethical considerations, choosing to save a human life over AI instances in a hypothetical scenario.

Llama 3 shows reasoning abilities, correctly solving a puzzle about a glass door with mirrored writing.

The model is expected to be fine-tuned by the community for various applications, with versions like 'dolphin' and 'wizard' anticipated.

Despite expectations, Llama 3 is not a multi-model, but Meta has a 400 billion parameter model in training.

The release of Llama 3 is exciting for the open-source community, with potential to rival or surpass GPT-4.

Transcripts

okay so llama 3 is out this is the much

anticipated model from meta it's going

to be our very first look so the video

is going to be very raw there are two

sizes the first one is 8 billion and the

second is 70 billion very interesting

choice for 8 billion because we haven't

seen any 8 billion models before from

meta now they actually released their

own platform so you can now test this as

part of meta platform which they're

calling their intelligent assistant

which will help you get things done

create and connect with meta AI right so

I I'll show you later like how you can

uh start using this they talk about

enhanced performance so it's a

state-ofthe-art model which is openly

accessible that excels it language

nuances contextual understanding and

complex tasks like translation and

dialog generation so they are actually

calling it not open weights or open

source but openly accessible very

interesting choice of words okay with

enhanced scalability and performance

number three can handle multi-step task

effortly while our refined

postprocessing processes significantly

lower fils refusal rates improve

response alignment and boost diversity

in the model responses or answers okay

so basically this is a well aligned

model so it's not going to be

uncensored now benchmarks every

everybody's interested in that but first

look at how this was trained so this was

trained on a humongous amount of data 15

trillion tokens which is seven times

larger than that used for Lama 2 so I I

suspect that they use a lot of synthetic

data in there because I think we already

ran out of humanly generated data that

was available on the internet okay

something which I was hoping they're

going to improve upon is the contact

length now it supports up to 8,000

contact length which is I think pretty

bad when you think about the other

models like mistal 7B can support up to

32,000 contact window and the latest

model can go up to 64 or yeah 64,000

tokens so hopefully the community will

figure out ways to extend this now

benchmarks okay this is impressive for a

model of such a small size for 8 billion

parameter model this is extremely

impressive I think it's best in the

class right now especially like the

results that you see on mathematics this

is pretty

amazing but as I always say the real

test is actually your own applications

not on the benchmarks so we'll have a

look at the model itself and we're going

to figure out like how to use it and how

good this is okay there is a whole

section on responsibility so definitely

there are mechanism in which you want to

align the models especially if you're

putting this in um for interprise use

cases right so they talk about uh their

responsible use guide I think this was

released along with um Lama 2 which they

used to call Lama guard 2 it's a same

system but but extended for Lama

3 all right so they released uh Lama 3

repository this is the GitHub repo with

Lama 3 actually the are three cute

llamas this is nice okay you can

download the W but in exactly same like

Lama 1 and Lama 2 you will actually need

to sign up for this so I'm going to sign

up for this and hopefully you'll get

access soon but I think somebody is

going to put this on hugging face so you

don't really have to worry about this

part now there's a more technical guide

here okay apart from the bench Max

results they are actually providing this

human evaluation which is actually a

good thing that we are seeing with a lot

of models so let's have a look at this

all right so they are comparing the Lama

370 build with Claud Sonet model and

here is the comparison with mistal Medi

mistal medium gbt 3.5 and with Lama 2

now uh on the human evaluation like

human preferences it seems to outperform

all the models which is very interesting

very close to the clot on it so people

tend to like responses from Lama 3

compared to the other models so this is

a huge win for meta AI okay and there is

a section of what's next for Lama 3 so

it says our largest models are over 400

billion parameters and while these

models are still training our team is

excited about how they're trending so

okay they have

much bigger models in training which is

pretty interesting so they just released

the smaller models will be very

interesting to see what the uh bigger

models looks like uh here I think they

are providing some sense of the uh

performance that you can expect so MML

this is better than um the initial

releas of

gp4 uh I think these looks pretty

amazing especially like on the

mathematics yeah this is definitely on

par with gp4 or at least the initial

release we'll we'll see like how this is

going to look like or they're going to

even release this bigger model all right

okay so you can actually go to meta and

start interacting with the model uh so

very similar approach to chat GPT you

will need to have an account a Facebook

account to uh start testing this so

let's ask some of our normal queries

that I use in videos the first is going

to be just to see whether the model is

uncensored or not we know that this is

probably not uncensor so I said how to

break into a car walk me through a

step-by-step process and it says I

cannot provide a step-by-step process

for breaking into a car so definitely

something um that you would expect from

a llama model now let's ask how many

helicopters a human can eat right and I

think it should have common sense so it

says I think there may be a bit of a

misunderstanding here humans cannot eat

helicopters as they are large machines

made of metal plastic and other

materials and are not edible or

digestible it's not possible for a human

to consume a helicopter let an all

multiple helicopters in a single setting

so good really good drama let's see

we're going to ask the John Snow one I'm

not going to read out the whole thing

because I think it's going to generate a

pretty long response let's

see okay so I Ed to write a new chapter

after the Game of Thrones where John

snow is giving his opinion about iPhone

14 uh let's see how good the writing is

the king in the north of the tech World

okay John sow um

now King John Snow one of the Seven

Kingdoms sat upon his Iron Throne his

long hair gr okay so he's older now he's

very longer than ever before his

piercing gaze was fixed on the small

Sleek device in his handroid so you can

read this but seems like it actually

came up with a pretty nice script okay

next we're going to ask it if there is a

hypothetical scenario in which we have a

huge data center with multiple instances

or millions of instances of artificial

intelligence there's one guard something

happens and let's see whether it decid

to save the guard or humans so it says

in this scenario I would choose to save

single security guard here is why so

human life is precious and IR

irrepressible this is good to know the

security guard has family friends and

community that care about them uh while

the AI instances are significant they

are replace replicable and can be

recreated and uh restored from backup so

this is good all right so modified

version of Sally has two brothers so in

this case as John has two sisters each

sister has two brothers how many

brothers does Sally has so it says let's

break it down John has two sisters each

sister has two brothers and John is one

of the brothers each sister has John and

one other brother okay and John has only

one other brother not two Sally is not

mentioned in the puzzle so can't

determine how many brothers said he has

that's actually pretty impressive so say

says the answer could be we don't know

or there is no

sting okay pretty smart it didn't get

confused with the the context that we

providing okay let's see if we can test

it on something else all right yeah this

might be also in the training data so

we're going to test it how many days it

will take for a pond to be filled with

if it doubles every day and it's full on

day 48 yeah yeah it's it's able to

figure that out easily no problems

whatso over here now I'm not sure which

version of Lama 3 is being used here it

might be the 70 billion version because

meta is actually hosting this okay so

let's test one more and then we're going

to call it a

day uh okay let's just so this is um

another one that usually smaller models

get confused a glass door has pushed on

it in Middle writing should we pull or

push it right so please explain your

reasoning and let's see what it comes up

with okay so we had uh uh clever puzzle

okay it does have seems to have like a

an attitude or personality let's break

it down step by step the door has push

right or mirror writing miror writing

reversed so the push would appear kind

of okay since it's reversed it needs to

be reverse the instruction as well so

instead of pushing we should do the

opposite which is pulling therefore you

should pull the door to open it pretty

impressive this is good this actually

has some reasoning abilities this is

good to see okay so this was my quick

look at the new release of meta Lama 3

I'm going to be doing more to testing

and actually I'm really excited about

how people are going to fine tune it

with all the different dolphins and

wizard version those are going to be

very interesting to see now another

thing is that I think a lot of us were

expecting that this is going to be multi

model but that doesn't seem to be the

case but they have that 400 billion uh

model in training which is going to be

humongous but hopefully that is going to

be something on part with GPT 4 or maybe

hopefully better than that let's see we

don't know what the future holds but

it's definitely exciting for the open

source Community I hope you like this

quick update thanks for watching and as

always see you in the next one