Metas LLAMA 405B Just STUNNED OpenAI! (Open Source GPT-4o)

Summary

Meta has unveiled Llama 3.1, a 45 billion parameter language model that excels in reasoning, tool use, and multilinguality. The model, which boasts a 1208 token context window, is the largest open-source model released to date, outperforming other models in various benchmarks despite its smaller size. Llama 3.1 also integrates with platforms like AWS and Nvidia, and is set to be deployed across Meta's apps. The research paper hints at future advancements, suggesting the potential for multimodal capabilities, further solidifying AI's role in solving complex challenges.

Takeaways

• 🚀 Meta has released Llama 3.1, a 45 billion parameter language model, which is the largest open source model ever released.
• 🔍 The 870 billion model has been updated with improved performance and capabilities.
• 🌐 Llama 3.1 shows significant improvements in reasoning, tool use, multilinguality, and a larger context window.
• 📈 Benchmark results for Llama 3.1 exceed expectations, with the model performing on par with or better than state-of-the-art models like GPT-4 and Claude 3.5.
• 📚 Meta has published a research paper detailing the improvements and capabilities of Llama 3.1.
• 🔄 Context window for all models has been expanded to 1208 tokens, allowing for handling larger code bases and more detailed reference materials.
• 🛠️ Llama 3.1 supports tool calls for functions like search, code execution, and mathematical reasoning, and has improved reasoning for better decision-making and problem-solving.
• 🌐 The model is designed to be scalable and straightforward, opting for a standard decoder-only transformer model architecture.
• 🖼️ Llama 3.1 is being integrated with image, video, and speech capabilities, aiming to make the model multimodal, although these features are still under development.
• 🌐 Llama 3.1 is available for deployment across platforms like AWS, Databricks, Nvidia, and GROQ, demonstrating Meta's commitment to open source AI.

Q & A

What is the significance of Meta releasing Llama 3.1?

Meta's release of Llama 3.1 is significant as it is a large language model with 45 billion parameters, marking it as one of the largest open-source models ever released. It brings improvements in reasoning, tool use, multilinguality, and a larger context window.

What updates did Meta make to their 870 billion parameter models?

Meta updated the 870 billion models with new, improved performance and capabilities, enhancing their reasoning, tool use, and multilingual abilities.

What are the notable features of the 405 billion parameter Llama model?

The 405 billion parameter Llama model offers impressive performance for its size, supports zero-sha tool usage, improved reasoning for better decision-making and problem-solving, and has an expanded context window of 1208 tokens.

How does Llama 3.1 compare to other state-of-the-art models in terms of benchmarks?

Llama 3.1 benchmarks are on par with or exceed state-of-the-art models in various categories, including tool use, multilinguality, and GSM 8K, showcasing its effectiveness despite having a smaller parameter size compared to models like GPT-4.

What is the context window size for the updated Llama models?

The context window for all updated Llama models has been expanded to 1208 tokens, allowing them to work with larger code bases or more detailed reference materials.

How does Meta's commitment to open source reflect in the Llama 3.1 release?

Meta's commitment to open source is evident in the Llama 3.1 release as they are sharing the models under an updated license that allows developers to use the outputs from Llama to improve other models, including synthetic data generation and distillation.

What is the potential impact of Llama 3.1 on AI research and development?

The release of Llama 3.1 could potentially drive advancements in AI research by enabling the creation of highly capable smaller models through synthetic data generation and distillation, thus fostering innovation and solving complex challenges.

How does Llama 3.1's architecture differ from other models like Gemini or GPT?

Llama 3.1 uses a standard decoder-only transformer model architecture with minor adaptations, opting for simplicity and training stability over more complex architectures like a mixture of experts, which is used in some other models.

What are the multimodal capabilities that Meta is developing for Llama 3?

Meta is developing multimodal extensions for Llama 3, integrating image, video, and speech capabilities via a compositional approach. These capabilities are still under development and not yet broadly released.

How does Llama 3.1 perform in human evaluations compared to state-of-the-art models?

In human evaluations, Llama 3.1 holds up well against state-of-the-art models, often winning or tying with them around 60% to 75% of the time, which is impressive considering its smaller size and cost-effectiveness.

What are the potential use cases for Llama 3.1's tool use capabilities?

Llama 3.1's tool use capabilities can be utilized for a wide range of applications, including executing code, generating tool calls for specific functions, and potentially becoming a key component in achieving more general intelligence by executing a wider range of tasks.

Outlines

00:00

🚀 Meta's Llama 3.1 Release

Meta has unveiled Llama 3.1, a 4.05 billion-parameter language model, which is the largest open-source model ever released. The model promises improvements in reasoning, tool use, multilinguality, and a larger context window. Meta also updated the 870 billion models with enhanced performance. The new models are designed to support various use cases, from enthusiasts to enterprises. The context window has been expanded to 1208 tokens, allowing for handling larger code bases and detailed reference materials. The models have been trained to generate tool calls for functions like search, code execution, and mathematical reasoning. Meta's commitment to open source is evident as they allow developers to use Llama's outputs to improve other models. The models will be deployed across platforms like AWS, Databricks, Nvidia, and Gock, with Meta AI users gaining access to the new capabilities across Facebook Messenger, WhatsApp, and Instagram.

05:00

📊 Llama 3.1's Benchmarks and Model Comparisons

The benchmarks for Llama 3.1's 4.05 billion parameter model show it performing on par with state-of-the-art models, even surpassing some in various categories. The model's efficiency is remarkable, given its smaller size compared to models like GPT-4, which is allegedly 1.8 trillion parameters. Llama 3.1 also outperforms other models in tool use, multilinguality, and the GSM 8K category. The reasoning score of 96.9 suggests superior reasoning capabilities compared to models like Claude 3.5 Sonic. Human evaluations further validate Llama 3.1's effectiveness, with the model either winning or tying with state-of-the-art models 60% to 75% of the time. Meta has also updated their 38 billion and 70 billion parameter models, making them the best in their respective sizes. The architectural choice of a standard decoder-only transform model, as opposed to a mixture of experts, has contributed to Llama 3.1's effectiveness.

10:00

🌐 Multimodal Capabilities and Future Improvements

Meta's research paper on Llama 3.1 discusses the integration of image, video, and speech capabilities into the model via a compositional approach, aiming to make it multimodal. Although these multimodal extensions are still under development, initial experiments show promising results, with the vision module outperforming GPT-4 Vision in some categories. The video understanding model also shows impressive performance, competing with larger multimodal models. Llama 3.1's longer token context length of 128 tokens enables more complex tasks. The model demonstrates tool use capabilities, such as analyzing CSV files and plotting time series graphs. Meta suggests that further improvements are on the horizon, indicating that Llama 3.1 is just the beginning of what's to come in AI model development. Users in the UK can access Llama 3.1 through the Gro platform, highlighting the model's accessibility and potential for widespread use.

Keywords

💡Llama 3.1

Llama 3.1 refers to a large language model developed by Meta, boasting 45 billion parameters. It is a central focus of the video, representing a significant advancement in AI capabilities. The model is highlighted for its improvements in reasoning, tool use, multilinguality, and a larger context window, as mentioned in the script when discussing the model's features and capabilities.

💡Benchmarks

Benchmarks in the context of the video are standardized tests used to evaluate the performance of the Llama 3.1 model against other state-of-the-art models. The script mentions that Llama 3.1 exceeds the previously previewed numbers, indicating superior performance in various categories such as tool use and multilinguality, which is crucial for assessing the model's capabilities.

💡Open Source

Open Source denotes that the Llama 3.1 model and its related tools are made publicly available, allowing anyone to use, modify, and distribute the software. The video emphasizes Meta's commitment to open source, sharing the new models under a license that encourages developers to utilize the model's outputs to improve other AI models, as stated in the script.

💡Parameters

Parameters, in the context of AI models, are the variables that the model learns from and adjusts during training. The script specifies that Llama 3.1 has '45 billion parameters,' which is a measure of the model's complexity and capacity for learning, highlighting the model's size and capability.

💡Tool Use

Tool Use refers to the model's ability to generate tool calls for specific functions like search, code execution, and mathematical reasoning. The script mentions that the models have been trained for this purpose, showcasing the model's advanced capabilities beyond traditional language processing.

💡Context Window

The Context Window is the amount of text the model can consider at one time to generate responses. The script notes that Meta has expanded the context window to 1208 tokens for their models, allowing them to work with larger code bases or more detailed reference materials, which is vital for complex tasks.

💡Multimodal

Multimodal refers to the integration of multiple types of data, such as image, video, and speech, into a single model. The script discusses Meta's experiments in making Llama 3 multimodal, aiming to enhance the model's capabilities to understand and process various forms of data beyond text.

💡Reasoning

Reasoning is the model's ability to make logical deductions or inferences. The script highlights an impressive reasoning score of 96.9 for the model, suggesting that its decision-making and problem-solving capabilities are notably advanced compared to other models.

💡Performance

Performance in this context refers to how well the Llama 3.1 model operates in terms of speed and accuracy. The video script discusses the model's improved performance, especially when comparing it to other models of different sizes, indicating that it is highly efficient.

💡Human Evaluation

Human Evaluation is the assessment of AI models based on human interaction and judgment. The script mentions that human evaluation is crucial for understanding how effective these models are in real-world use, as it provides insight into user experience and model usability.

💡Synthetic Data Generation

Synthetic Data Generation is the process of creating artificial data that mimics real-world data for training AI models. The script indicates that this will be a popular use case enabled by the open-source nature of Llama 3.1, allowing for the creation of smaller, highly capable models.

Highlights

Meta has released Llama 3.1, a 45 billion parameter language model.

Llama 3.1 is the largest open source model ever released.

Improvements in reasoning, tool use, multilinguality, and context window.

Benchmark numbers exceed previous previews.

Updated 8B and 70B models for various use cases.

Expanded context window to 1208 tokens for all models.

Models trained to generate tool calls for specific functions.

Support for zero sha tool usage and improved reasoning.

New system level approach for balancing helpfulness and safety.

Partnerships with AWS, Databricks, Nvidia, and Grock for deployment.

Open source commitment with an updated license for model outputs.

Synthetic data generation and distillation as potential use cases.

Llama 3.1 to be rolled out to Meta AI users and integrated into Facebook Messenger, WhatsApp, and Instagram.

Benchmarks show Llama 3.1 is on par with state-of-the-art models.

Llama 3.1 outperforms GPT-4 and Claude 3.5 in tool use and multilinguality.

Human evaluations show Llama 3.1 holds up against state-of-the-art models.

Llama 3.1 achieves a 4.5 times reduction in size compared to GPT-4.

Llama 3.1's architecture focuses on scalability and straightforwardness.

Llama 3.1 integrates image, video, and speech capabilities.

Llama 3.1 vision module performs competitively with state-of-the-art models.

Llama 3.1 video understanding model outperforms Gemini 1.0 Ultra and GPT-4 Vision.

Llama 3.1 supports natural speech understanding for multi-language conversations.

Llama 3.1 demonstrates tool use capabilities with CSV analysis and time series plotting.

Meta suggests further improvements are on the horizon for Llama models.

Llama 3.1 is available in the UK through Gro, an influence platform.

Transcripts

So Meta have finally released their

highly anticipated llama 3.1

45 billion parameters large language

model there's so much to discuss and so

much that they actually spoke about in

their research paper so first of all

what you're going to watch is their

announcement video and then I'm going to

dive into so many of the details they

seemingly left out and including the

stunning bench today we're excited to

deliver on the long awaited llama 3.1

405 billion parer model that we

previewed back in April we're also

updating the 870 billion models with new

improved performance and capabilities

the 405 is hands down the largest and

most capable open source model that's

ever been released it lands improvements

in reasoning tool use multilinguality a

larger context window and much more and

the latest Benchmark numbers that we're

releasing today exceed what we previewed

back in April so I encourage you to read

up on the details that we've shared in

our newly published research paper

alongside the 405b model we're releasing

an updated collection of pre-trained and

instruction tuned 8B and 70b models to

support use cases ranging from

enthusiasts and startups to Enterprises

and research Labs like the 405b these

new 8 and 70b models offer impressive

performance for their size along with

notable new capabilities following

feedback we heard loud and clear from

the community we've expanded the context

window of all of these models to 1208

tokens this enabled ables the model to

work with larger code bases or more

detailed reference materials these

models have been trained to generate

tool calls for a few specific functions

like search code execution and

mathematical reasoning additionally they

support zero sha tool usage improved

reasoning enables better decision-making

and problem solving updates to our

system level approach make it easier for

developers to balance helpfulness with

the need for safety we've been working

closely with Partners on this release

and we're excited to share that in

addition to running the model locally

the now be able to deploy llama 3.1

across Partners like AWS datab bricks

Nvidia and grock and it's all going live

today at meta we believe in the power of

Open Source and with today's release

we're furthering our commitment to the

community our new models are being

shared under an updated license that

allows developers to use the outputs

from llama to improve other models this

includes outputs from 405b we expect

synthetic data generation and

distillation to be a popular use case

that enables new possibilities for

creating highly capable smaller models

and helping to advance AI research

starting today we're rolling out llama

3.1 to meta AI users and we're excited

to bring many of the new capabilities

that Angela outlined to users across

Facebook Messenger WhatsApp and

Instagram with the release of 3.1 we're

also taking the next steps towards open-

Source AI becoming the industry standard

continuing in our commitment to a future

where greater access to AI models can

can help ecosystems Thrive and solve

some of the world's most pressing

challenges we look forward to hearing

your feedback and seeing what the

developer Community will build with

llama so that was the announcement video

from meta but like I said there's

actually so much to dive into here and I

think genuinely that this release is

going to change the entire ecosystem so

one of the things that most people did

want to know was of course the

benchmarks for L 3

405b so when we actually take a look at

some of these benchmarks when one of the

things that we can see here is that this

is actually on par with state-of-the-art

models and something funny that I did

actually find here was that Gemini 1.5

Pro isn't even here so I'm guessing that

maybe that model is far superior in

those areas but what we can see here

across the board and if you want just a

quick glance essentially the categories

the Llama bests the other models in are

the categories where it has the Box

around um and I think it's crazy that

currently what we're looking at here is

a model that actually bests GPT 40 and

clae 3.5 Sonet in many different

category one of those being tool use and

multilingual and of course the GSM 8K

which is pretty crazy and arguably you

can see that the reasoning of this model

is up to

96.9 which means that potentially the

reasoning of this model is better than

clae 3.5 Sonic now of course this is all

well and good you know having benchmarks

that showcase that your model model is

doing amazing things but one of the

things we do have to always look at is

of course the human evaluation as after

all these models will be used natively

by humans and that is by far the most

effective Benchmark to seeing how

effective these models truly are but

just on the surface level taking a look

at what we do have here from a

completely open model and considering

the fact that these other models are

much larger in size as you do know GPT 4

allegedly was 1.8 trillion parameters

meaning that if we compare that size to

llama 3.1 being a 405 billion parameter

model that means that it is as good or

if not better than GPT 4 with a 4.5

times reduction in size which is just

completely remarkable meaning that

potentially people can have GPT 4

running offline locally although yes

it's going to be pretty computer

intensive but this is something that is

truly shocking because it shows us the

trajectory that we're on in terms of the

size versus efficiency so I do think

that this is genuinely the start of a

new paradigm where we start to get

Frontier capabilities available for free

now what we also did get from llama 3

was this right here so you can see that

they also did updated versions of their

llama 38 billion parameter model and the

70 billion parameter model which means

that they made even further improvements

now what this basically just means here

is that in their respective sizes llama

3 is by far the best model that you can

use you can see that Gemma 2 by Google

here is falling short in nearly every

single category other than the arc

challenge reasoning and we've got mixol

here that also is falling short and of

course you can see that llama 3.1 the 70

billion parameter model actually does

far better than Mixr which is 8 * 22

billion parameters mixture of experts

and GPT 3.5 turbo and to be honest what

I'm seeing here is that this llama 3.1

model it isn't just marginally better

than the other models at the respective

sizes we can see that not only does it

surpass them in all of the categories it

manages to surpass them in a clear

margin which is incredible like

genuinely incredible so overall if you

are someone that is using these small

models for whatever tools that you might

want to use them for you can see that

llama 3.1 a 70 billion parent model is

super effective now like I said before

if we look at the human evaluations for

this model what we can see here that it

does hold up respectively against

state-of-the-art models what we can see

here is that around 70% of the time to

60% of the time or 70% to 75% of the

time it either wins or ties the state of

the-art models that is really impressive

considering the size difference and the

cost to use these models I mean imagine

having an unlimited version of Claude

3.5 Sonic I know so many people that are

building with those models that

unfortunately run into issues because

the model is just very expensive to use

so this shows us here that versus gp4 it

wins a lot more and vers versus GPT 40

it does win a little bit less but it's

still very respective considering how

small the model is now I know it's still

pretty big but compared to the other

model sizes this is just something that

we never thought we'd see now something

interesting that they also managed to

talk about was they managed to talk

about how this model was a bit different

in terms of the architecture so we can

see here that they said we've made

design choices that focus on keeping the

model development process scalable and

straightforward we've opted for a

standard decoder only transform model

architecture with minor adapt

rather than using a mixture of experts

model to maximize training stability so

I'm guessing that for whatever reason

here and of course the reason is that

they stated that they wanted to keep

everything super simple they decided

against using a mixture of experts model

and we can see here that that this made

the model a lot more effective and I'm

wondering if this is going to be a

continued Trend as we move towards a

space because I did see a recent paper

in which they actually did talk about

and this was Google not meta but they

actually did talk about a million

experts so I'm wondering if this is just

for open source models but it will be

interesting to see what continues on so

this is where we get into the research

part of this and you can see here that

they talk about the Llama 3 her of

models so it says here that the paper

also presents the results of experiments

in which we integrate image video and

speech capabilities into llama 3 via

compositional approach now that is

absolutely insane because what they're

trying to do here is to make this model

multimodal and what you can see here is

that they say we observe this approach

performs competitively with

state-ofthe-art on image video and

speech recognition tasks the resulting

models are not yet being broadly

released as they are still

underdeveloped so essentially what they

have is they have image video and speech

recognition task which they can use but

these are still under development and

some of the stuff that I'm seeing in

this research paper shows me that

they're actually pretty good so what we

can see here is that they said as part

of the three L 3 development process

we've also developed multimodal

extensions to the model enabling image

recognition video recognition and speech

understanding capabilities they're still

under active development and not yet

ready for release in addition to our

language modeling results the paper

presents our initial experiments with

those multimod model so what you can see

here is llama 3 vision and we can see

that this model actually does really

well at Vision tasks and some of them it

even manages to surpass state-of-the-art

models so it says image understanding

and performance of our Vision module

attached to llama 3 so this looks rather

effective because there aren't too many

differences in terms of how it performs

we can see that it performs a lot better

than GPT 4 Vision if you take a look

actually at GPT 4 Vision you can see

that in these categories even at the ai2

diagram you can see that this is 94.1

and this is 78.2 so taking a look here

you can see that this actually does

better than the previous GPT 4 vision

and the reason that it's crazy is

because if you remember reading the

initial GPT 4 Vision paper that paper

was actually talking about how crazy GPT

4 Vision was so I can't imagine all of

the use cases that are going to happen

when we actually do get llama 3 as a

vision assistant so that's going to be

really amazing and what's even crazier

is that there were only marginal

improvements from llama 370 billion

parameters to llama 345 billion paramet

so we can see here that using these

different models like there's not that

much discrepancy between how much the

vision models are between the 70 billion

and the 45 billion PR but overall this

is really good because image recognition

is relatively expensive now we've also

got video understanding and what's

impressive here is that if we actually

look at llama 370 billion parameter

model the video understanding model that

video understanding model actually

performs better than Gemini 1.0 Ultra

Gemini 1.0 Pro Gemini 1.5 Pro gp4 V and

gbt 40 so that's pretty incredible that

they managed to supply deia in ter terms

of the video understanding model and I

got to be honest whilst yes you could

argue that Gemini 1.5 Pro the video

understanding is long context so it's

kind of different in the sense that it

can understand what's going on over 2

million tokens I still find it

incredible that such a small model is

able to compete and be on par with these

giant multimodal model now additionally

what we can see here is one of the

features that they actually spoke about

which is essentially the audio

conversations so you can see right here

this is a screenshot from where someone

is having a conversation out loud I

guess that you could say this is quite

similar to GPT 40 you know the version

of chat gbt that you can actually talk

to like it's a person but you can see

here that it's pretty crazy in the sense

that it's able to understand many

different languages and it's able to

understand that through natural speech

and not just text which is a little bit

different because understanding the

pronunciations of certain words and of

course the under and of course how those

words are spoken is a really big thing

in terms of using AI now another thing

that they also showed was this tool use

and we can see right here that if we

actually take a look at what's going on

it says you know can you describe what's

in this CSV then the model is able to

identify exactly what's going on in this

CSV which is really nice because a

feature that I didn't mention was

actually that llama 3 is actually 128

tokens long so it's a longer token

context length model and then you can

see right here it says can you PL it on

a Time series so what it's also able to

do is it's also able to use tools to

execute different things so you can see

right here that the model is able to

essentially bring up this graph which is

really nice and then it's able to say

you know can you plot the S&P 500 over

the time in the same graph and then it's

able to do that rather effectively now I

think you guys might underestimate

what's going on here because to use is

truly the next stage of these AI systems

and I think this is truly how we get to

systems that are you know generally

intelligent because they're able to

execute a wider range of things you

utilizing all of the tools the last

thing that I'm going to leave you guys

with which is pretty crazy is that they

state that our experience in developing

llama 3 suggest that substantial further

improvements of these models are on the

horizon which means that they're

basically saying that look llama 3 is

not the best that we've going to give

you there are so many improvements that

we can make to AI models and we are just

scratching the surface of what's going

on now if you enjoyed this video and you

want to use LL 3 of course in you're in

America you can just head on over to

meta but if you're in the UK the only

place that I know currently that you can

use this and I've even tried with a VPN

and it doesn't work cuz you need an

account to sign in and of course by the

time this video is released that might

have changed but currently if you want

to use it right now is after the video

is released and you're in the UK you're

going to have to use Gro which is a

influence platform where they basically

have super fast inference then just head

on over to this right here you can see

llama 3 45 billion parameters then of

course you can use the model right here

so that's the only way you can use it in

the UK I'm not sure if it's banned in

other regions but I do know that you

know currently uh meta AI is just not

available right now in the UK but of

course they're going to roll it out on

many different platforms that you know

you're going to be able to serve it so

within 24 hours that's not going to be a

problem there's a billion different

sites that are going to start hosting

this but of course if you did enjoy the

video hopefully this was of some use to

you and I'll see you guys in the next

one