Llama 3.2 is HERE and has VISION 👀

Summary

Meta has unveiled Llama 3.2, an upgrade to its AI model with added vision capabilities. The new models include an 11 billion parameter and a 90 billion parameter version, both with vision, and text-only models of 1 billion and 3 billion parameters for edge devices. These are designed to run tasks like summarization and rewriting locally. Llama 3.2 also introduces the Llama stack for simplified development and is optimized for Qualcomm processors. Benchmarks show it outperforming peers in its class.

Takeaways

• 🚀 **Llama 3.2 Release**: Meta has released Llama 3.2, an upgrade from Llama 3.1, with new capabilities.
• 👀 **Vision Capabilities**: Llama 3.2 introduces vision capabilities to the Llama family of models.
• 🧠 **Parameter Sizes**: Two new vision-capable models are available, one with 11 billion parameters and another with 90 billion parameters.
• 🔩 **Drop-in Replacements**: The new models are designed to be drop-in replacements for Llama 3.1, requiring no code changes.
• 📱 **Edge Device Models**: Two text-only models (1 billion and 3 billion parameters) are optimized for edge devices like smartphones and IoT devices.
• 🌐 **AI at the Edge**: The script emphasizes the trend of pushing AI compute to edge devices.
• 📊 **Performance Benchmarks**: Llama 3.2 models outperform their peers in benchmark tests for summarization, instruction following, and rewriting tasks.
• 🔧 **Optimized for Qualcomm**: The models are optimized for Qualcomm and MediaTek processors, indicating a focus on mobile and edge computing.
• 🛠️ **Llama Stack Distributions**: Meta is releasing Llama Stack, a set of tools to simplify working with Llama models for production applications.
• 📈 **Synthetic Data Generation**: Llama 3.1 is used to generate synthetic data to improve the performance of Llama 3.2 models.
• 🔎 **Vision Task Support**: The largest Llama 3.2 models support image reasoning for tasks like document understanding and visual grounding.

Q & A

What is the main update in Llama 3.2?

Llama 3.2 introduces vision capabilities to the Llama family of models, allowing them to 'see' things. This is a significant update from the previous versions which were text-only.

What are the different versions of Llama 3.2 models mentioned in the script?

The script mentions four versions: an 11 billion parameter version with vision capabilities, a 90 billion parameter version with vision capabilities, a 1 billion parameter text-only model, and a 3 billion parameter text-only model.

What does 'drop-in replacement' mean in the context of Llama 3.2 models?

A 'drop-in replacement' means that the new models can be used in place of the older Llama 3.1 models without requiring any changes to the existing code.

What is special about the 1 billion and 3 billion parameter models?

These models are designed to be run on edge devices, such as smartphones, computers, and IoT devices. They are optimized for on-device AI compute, which is a growing trend in the industry.

What are some use cases for the Llama 3.2 models?

The Llama 3.2 models are capable of tasks like summarization, instruction following, rewriting tasks, and image understanding tasks such as document level understanding, image captioning, and visual grounding.

How does the Llama 3.2 model compare to its peers in terms of performance?

The script suggests that the Llama 3.2 models, especially the 3 billion parameter version, perform incredibly well compared to models in the same class, such as GPT-J 2B and 53.5 Mini.

What is the significance of the Llama Stack distributions released by Meta?

The Llama Stack distributions are a set of tools that simplify how developers work with Llama models in different environments, enabling turnkey deployment of applications with integrated safety.

What are the capabilities of the Llama 3.2 models with vision?

The 11 billion and 90 billion parameter models support image reasoning, including document understanding, image captioning, and visual grounding tasks.

How did Meta achieve the integration of vision capabilities into the Llama 3.2 models?

Meta trained a set of adapter weights that integrate the pre-trained image encoder into the pre-trained language model, using a new technique that involves cross attention layers.

What is the purpose of the synthetic data generation mentioned in the script?

Synthetic data generation is used to augment question and answer pairs on top of in-domain images, leveraging the Llama 3.1 model to filter and augment data, which helps improve the model's performance.

How are the smaller 1 billion and 3 billion parameter models created from the larger Llama 3.2 models?

The smaller models are created using a combination of pruning and distillation methods on the larger 11 billion parameter model, making them lightweight and capable of running efficiently on devices.

Outlines

00:00

🚀 Meta Connect's Llama 3.2 Announcement

Meta Connect has introduced Llama 3.2, a significant update to the Llama AI model family. Llama 3.2 introduces Vision capabilities to the model, allowing it to process visual information. Two new sizes are available: an 11 billion parameter version and a 90 billion parameter version, both of which can replace Llama 3.1 without requiring code changes. Additionally, Meta has released two text-only models with 1 billion and 3 billion parameters, designed for edge devices such as smartphones and IoT devices. The video creator emphasizes the importance of AI computation moving to edge devices and how these new models align with this trend. The models are pre-trained and instruction-tuned, offering state-of-the-art performance in tasks like summarization and rewriting, all capable of running locally.

05:02

📊 Llama 3.2 Benchmarks and Vision Capabilities

The script discusses benchmarks comparing Llama 3.2's tiny models (1B and 3B) with other models like GPT-J and Gopher, showing Llama 3.2 performs exceptionally well. The larger vision-enabled models are also compared against models like CLAUDE 3 and GPT-40, with Llama 3.2's 90B model leading in most categories. The video creator tests the 1B model's speed, which generates over 2000 tokens per second, and successfully writes a Python snake game on the first attempt. The largest Llama 3.2 models (11B and 90B) support advanced image reasoning tasks, such as understanding documents, image captioning, and visual grounding. The new models integrate an image encoder with the language model using a novel adapter approach, preserving the text capabilities of Llama 3.1. Post-training involves alignment, fine-tuning, and synthetic data generation. The video concludes with the creator's excitement for on-device AI and intention to test the models further in upcoming videos.

Keywords

💡Llama 3.2

Llama 3.2 refers to the latest version of the Llama AI model series, which now includes vision capabilities. This update is a significant leap from its predecessors, Llama 3.0 and 3.1, and it's a central theme of the video. The script mentions that Llama 3.2 comes in two sizes: an 11 billion parameter version and a 90 billion parameter version, both of which are equipped with vision capabilities, making them more versatile and powerful for various applications.

💡Vision Capabilities

Vision capabilities refer to the ability of AI models to process and understand visual information. In the context of the video, Llama 3.2's new vision capabilities allow the model to 'see' and analyze images, which is a groundbreaking feature compared to previous text-only models. This enhancement positions Llama 3.2 as a multimodal model capable of both text and image understanding.

💡Parameter Version

A parameter version in AI refers to a specific configuration of a model defined by the number of parameters it has. The script introduces two new parameter versions of Llama 3.2: 11 billion and 90 billion. These versions are described as 'drop-in replacements' for the previous Llama 3.1 models, meaning they can be used interchangeably without needing to alter existing code, which is a significant advantage for developers.

💡Edge Devices

Edge devices are non-cloud-based computing devices like smartphones, computers, and IoT devices. The video emphasizes the importance of pushing AI compute to edge devices for faster and more efficient processing. The script mentions two new text-only models (1 billion and 3 billion parameters) designed specifically for edge devices, highlighting a trend towards more capable, smaller AI models that can operate locally.

💡Pre-trained and Instruction Tuned

Pre-trained models are AI models that have been trained on large datasets before being fine-tuned for specific tasks. 'Instruction Tuned' refers to the process of further training these models to follow instructions or commands. The video script mentions that the new Llama 3.2 text-only models are pre-trained and instruction tuned, ready for use out of the box, which means they can perform tasks like summarization, following instructions, and rewriting tasks efficiently.

💡Context Windows

Context windows refer to the amount of context an AI model can consider when generating a response. The script specifies that the 1 billion and 3 billion parameter versions of Llama 3.2 have a context window of 128k out of the box. This is significant because a larger context window allows the model to process more information at once, which can lead to more accurate and relevant responses.

💡Qualcomm

Qualcomm is a company that designs processors used in many edge devices. The video mentions a partnership between Meta and Qualcomm, emphasizing the optimization of the Llama 3.2 models for Qualcomm and MediaTek processors. This collaboration is part of the push towards on-device AI compute, ensuring that the models run efficiently on a wide range of devices.

💡Llama Stack

Llama Stack is a set of tools introduced in the video that developers can use to work with Llama models and build applications around them. It simplifies the process of deploying applications with integrated safety and tooling. The script describes Llama Stack as enabling 'TurnKey deployment' of various applications, which means it provides a straightforward way to develop and deploy applications using Llama models.

💡Benchmarks

Benchmarks in the context of AI refer to standardized tests that measure the performance of models. The video script provides benchmarks comparing Llama 3.2 models to other models like GPT and CLAUD. These comparisons show how Llama 3.2 performs in tasks such as summarization and tool use, which is crucial for understanding the model's capabilities relative to its peers.

💡Image Reasoning

Image reasoning is the ability of AI models to understand and draw conclusions from images. The video script highlights that the largest Llama 3.2 models (11b and 90b) support image reasoning for tasks like document understanding and visual grounding. This means they can analyze images, including charts and graphs, and answer questions based on visual content, which is a significant advancement in AI capabilities.

💡Adapter Weights

Adapter weights are a technique used to integrate new capabilities into a pre-trained AI model without extensive retraining. The video explains that adapter weights were used to add image input support to the Llama 3.2 models. This involved training a set of cross-attention layers to align image representations with language representations, which allowed the models to become vision-capable without losing their text-based intelligence.

Highlights

Meta Connect event introduced Llama 3.2, an update to the Llama AI model series.

Llama 3.2 introduces Vision capabilities to the Llama family of models.

Two new sizes of Llama 3.2 are available: 11 billion parameter and 90 billion parameter versions.

Llama 3.2 models are drop-in replacements for Llama 3.1, requiring no code changes.

Two text-only models were introduced: 1 billion and 3 billion parameters, designed for edge devices.

Edge devices include smartphones, computers, and IoT devices.

Llama 3.2's 1 billion and 3 billion parameter models support 128k context windows.

Llama 3.2 models are state-of-the-art in tasks like summarization and rewriting, running locally.

Meta is partnering with Qualcomm to push AI compute to edge devices.

Llama 3.2 models are optimized for Qualcomm and Mediatek Tech processors out of the box.

Llama 3.2 vision models are drop-in replacements for text models and excel at image understanding.

Meta is investing in ecosystem building with tooling for fine-tuning and hosting open-source models.

Llama Stack distributions are released to simplify working with Llama models.

Llama 3.2 can be downloaded from llama.com or Hugging Face, and is available on various cloud platforms.

Benchmarks show Llama 3.2 outperforming peers in its class for on-device models.

Llama 3.2's largest models support image reasoning for document understanding and visual grounding tasks.

A new model architecture was developed to support image reasoning in Llama 3.2.

Llama 3.2 uses adapter weights to integrate image encoder representations into the language model.

Post-training alignment and synthetic data generation were used to enhance Llama 3.2's capabilities.

Llama 3.2's 1 billion and 3 billion parameter models were created using pruning and distillation techniques.

Meta's release of Llama 3.2 is a significant step towards on-device AI compute.

Transcripts

meta connect just happened and meta just

dropped llama 3.2 we have a new model

new sizes Vision capabilities and so

much more so that's what we're going to

go through today and thank you to meta

for partnering with me on this video so

I'm going to talk about the highlights

right away get you that information

immediately and then I'm going to go

more in depth on these topics in a

moment so first llama 3.2 that's the big

news llama 3.1 was a huge improvement

over llama 3.0 and now we have 3.2 what

what's different about llama 3.2 well

now llama has Vision llama can actually

see things and that is an incredible

update to the Llama family of models we

have an 11 billion parameter version and

a 90 billion parameter version of their

new vision capable models and these are

dropin Replacements to llama 3.1 which

means you don't have to change any of

your code if you're already using it you

don't have to really change anything you

simply drop in these new models they're

different sizes but they have all the

capabilities of the text based

intelligence and now they also have

vision-based intelligence they also

dropped two text only models that are

tiny 1 billion and 3 billion these are

specifically made to be run on edge

devices now if you've been watching my

videos at all you know I really believe

in AI compute getting pushed to Edge

devices and what are Edge devices cell

phones computers Internet of Things

devices basically anything that's not in

the the cloud and I truly believe more

and more AI compute is going to be

pushed to Edge devices and this is a

huge step in that direction models are

becoming much more capable at a much

smaller size and that's what we're

seeing here llama 3.2 1 billion and 3

billion parameter Texton versions these

are pre-trained and instruction tuned

ready to go so I can imagine these

fitting easily into the meta AI Rayband

glasses the 1 billion and 3 billion

parameter versions are are 128k context

windows out of the box and they are

state-of-the-art compared to their peers

on use cases like summarization

instruction following rewriting tasks

all again running locally and this again

confirms what I really believe the

future of AI looks like which is a bunch

of really small capable specialized

models that can run on device so

specifically for these models they're

really good at these types of tasks and

if you remember when I worked with

Qualcomm on that video Qualcomm was very

much about pushing AI compute to Edge

devices and of course meta is partnered

with Qualcomm on this and these models

are ready to go out of the box optimized

for Qualcomm and mediate Tech processors

as I said supported by a broad ecosystem

llama 3.2 11b and 90b vision models are

drop in replacements for their

corresponding text model equivalence

while exceeding on image understanding

tasks compared to closed models such as

clad 3 ha coup now you know I'm going to

be testing all of these models in

subsequent videos so make sure you're

subscribed to see those tests

additionally unlike other open

multimodal models both pre-trained and

aligned models are available to be

fine-tuned for custom applications using

torch tune and deployed locally using

torch chat and they're also available to

try using our smart assistant meta AI

now it's clear that meta is investing a

ton into their ecosystem building out

the tooling to find tune and services to

host and basically everything that you

need to have an open-source model in

your personal life or your business

they're also releasing their first llama

stack distributions and that is a set of

tools that developers can use to work

with the Llama models and build

everything around the core llm that is

necessary to build production level

applications here it describes llama

stack as a way to greatly simplify the

way developers work with llama models in

different environments including single

node on Prem cloud and on device

enabling TurnKey deployment of retrieval

augmented generation and tooling enabled

applications with integrated safety and

looking at the open source of course

llama stack GitHub repo here are the

things that it supports inference safety

memory agentic system evaluation

posttraining synthetic data generation

and reward scoring each of those has a

rest endpoint that you can use easily so

you can download llama 3.2 from

llama.com or hugging face and it's going

to be available on some of meta's cloud

Partners including AMD AWS datab bricks

Dell Google Cloud grock IBM Intel Azure

Nvidia Oracle Cloud Snowflake and more

all right now let's look at some of the

benchmarks so here are some benchmarks

in this column and then in this Row the

different models that it's comparing

against so here's llama 3.2 1B and llama

3.2 3B versus Gemma 2B and 53.5 mini so

these are comparing these small on

device models and as we can see this

llama 3.2 3B model actually performs

incredibly well versus their peers in

the same class of models here's MML U at

63 gsmk at 77 here's the ark challenge

at 78 and here's one for Tool use so

here's Nexus and bf clv2 really really

good for being such a small model now

let's look at the larger variants that

have Vision enabled so here we have

llama 3.29 B and 11b and comparing

against Claud 3 Hau and GPT 40 mini and

the Llama 3.2 90b seems to be the Best

in Class almost across the board so

let's test the tiny model first I'm on

gro.com llama 3.2 1B preview right there

and let's see how fast this thing is

going to go write me a story oh my God

2,000 plus tokens per second look at

that let's give it something a little

bit more specific now let's just see if

we can do it it write the Game snake in

Python okay there it is 2,000 tokens per

second and we'll see if it actually

works oh look at that it worked

unbelievable with 2,000 tokens per

second a total output time of less than

1 second a 1 billion parameter model got

the snake game on the first try very

very impressive so I'm going to save the

vision test for another video but for

now let me tell you a little bit more

about it the two largest models of the

Llama 3.2 collection 11b and 90b support

image reasoning use cases such as

document level understanding including

charts and graphs captioning of images

and visual grounding tasks such as

directionally pinpointing objects in

images based on natural language

descriptions for example a person could

ask a question about which month in a

previous year their small business had

the best sales and llama 3.2 can then

reason based on an available graph and

quickly provide the answer now I

definitely want to try the worlds Waldo

with this Vision model as the first

llama models to support Vision task the

11b and 90b models required an entirely

new model architecture that supports

image reasoning to add image input

support we trained a set of adapter

weights that integrate the pre-trained

image encoder into the pre-trained

language model so built right into that

core model but they used a new technique

to do so the adapter consists of a

series of cross attention layers that

feed image encoder representations into

the language model we trained the

adapter on text image pairs to align the

image representations with the language

representations during adapter training

we also updated the parameters of the

image encoder but intentionally did not

update the language model parameters by

doing that we keep all the Texton

capabilities intact providing developers

a dropin replacement for llama 3.1

models so again it's going to be just as

good as llama 3.1 text models but now it

also has vision and if you want to read

more about the details of how they

actually achieved this I will drop links

to everything in the description below

in posttraining they did several rounds

of alignment on supervised fine tuning

rejection sampling and direct preference

optimization DPO they leverage synthetic

data Generation by using the Llama 3.1

model to filter and augment question and

answers on top of in domain images so

synthetic data is here it is here and it

is ready and llama 3.1 is capable of it

let alone llama 3.2 now they also use

llama 3.1 the larger one as a teacher

model to teach a much smaller version

and that's how we got the 1 and 3

billion parameter llama 3.2 versions

they use two methods pruning and

distillation on the 1B and 3B models

making them the first highly capable

lightweight llama models that can fit on

devices efficiently I am 100% behind on

device AI compute so that's it congrats

The Meta on another fantastic open

source release I am going to be testing

all of these different models I'm going

to create two different test videos one

for testing the text intelligence and

then one for testing the vision

intelligence thanks again to meta for

partnering with me on this video If you

enjoyed this video please consider

giving a like And subscribe and I'll see

you in the next one