Llama 3.2 is HERE and has VISION đ
Summary
TLDRMeta 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
- đ **Llama 3.2 Release**: Meta has released Llama 3.2, an upgrade from Llama 3.1, with new capabilities.
- đ **Vision Capabilities**: Llama 3.2 introduces vision capabilities to the Llama family of models.
- đ§ **Parameter Sizes**: Two new vision-capable models are available, one with 11 billion parameters and another with 90 billion parameters.
- đ© **Drop-in Replacements**: The new models are designed to be drop-in replacements for Llama 3.1, requiring no code changes.
- đ± **Edge Device Models**: Two text-only models (1 billion and 3 billion parameters) are optimized for edge devices like smartphones and IoT devices.
- đ **AI at the Edge**: The script emphasizes the trend of pushing AI compute to edge devices.
- đ **Performance Benchmarks**: Llama 3.2 models outperform their peers in benchmark tests for summarization, instruction following, and rewriting tasks.
- đ§ **Optimized for Qualcomm**: The models are optimized for Qualcomm and MediaTek processors, indicating a focus on mobile and edge computing.
- đ ïž **Llama Stack Distributions**: Meta is releasing Llama Stack, a set of tools to simplify working with Llama models for production applications.
- đ **Synthetic Data Generation**: Llama 3.1 is used to generate synthetic data to improve the performance of Llama 3.2 models.
- đ **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
đ 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.
đ 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.
Mindmap
Keywords
đĄLlama 3.2
đĄVision Capabilities
đĄParameter Version
đĄEdge Devices
đĄPre-trained and Instruction Tuned
đĄContext Windows
đĄQualcomm
đĄLlama Stack
đĄBenchmarks
đĄImage Reasoning
đĄAdapter Weights
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
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