Hey everyone and welcome back.
Today we're gonna be taking a deep dive
into a paper that's been making some waves in the AI world.
Oh yeah, I saw that one.
DeepSeq V3,
scaling open source language models with long termism.
DeepSeq V3 is a new open source large language model.
And LLM. LLM.
And it's designed to be really efficient,
cost effective and perform really well.
Especially when it comes to things like code and math.
Yeah, they're really trying to challenge those
big closed source models we hear about all the time.
It's really cool to see open source models
kind of pushing the boundaries.
Yeah, making it more accessible to everybody.
Right, so let's unpack what makes DeepSeq V3 so special.
Okay.
They start off the paper,
talking about this need for these open source models
in the AI landscape.
And they present DeepSeq V3 as their answer to that call.
Yeah, that it's crucial for driving innovation
and making AI more democratic, collaborative.
I like that, democratizing AI.
It shouldn't just be for the big players, right?
So how do they actually build this impressive model?
Well, they started with that familiar framework
called Transformer, which is pretty standard for LLMs.
You can think of it as the blueprint
for how the model processes and understands language.
So a foundation.
Exactly, a foundation.
But on top of this foundation,
they made some really clever tweets to help it stand out.
For instance, they use something called multi-head latent attention
or MLA.
MLA.
Okay, no, that's mouthful.
Yeah, it sounds intimidating a little bit.
Can you break that down?
Yeah, so think of it this way.
Imagine the model has this massive filing cabinet
full of information.
Okay.
MLA is like a super efficient filing system.
Okay.
That helps the model find the information it needs much faster.
So making it more efficient.
Exactly.
This means DeepSeq v3 can run on less powerful hardware,
making it more accessible for developers and researchers.
I see.
Who might not have access to those giant supercomputers.
So it's all about kind of making things leaner and faster.
Exactly.
And they didn't stop there.
They also incorporated Depseqbo.
Okay.
Which uses a mix of shared and routed experts.
Oh, experts like having different parts of the models
specialized in different tasks?
That's a great way to put it.
Yeah.
It's like having a team of specialists all working together.
I see.
Instead of one giant model trying to do everything,
this approach improves training efficiency.
Okay.
Meaning they can train DeepSeq v3 faster
and use fewer resources.
So MLA is like the efficient filing system.
And DeepSeqbo is like the team of specialized experts.
Exactly.
I like that.
What else did they do to make this model unique?
One really cool innovation is multi-token prediction or MTP.
MTP.
So think of traditional models as someone reading a sentence
one word at a time and always guessing the next word.
Okay, yeah.
With MTP, DeepSeq v3 can actually predict
multiple words at once.
Wow.
Making it more efficient and potentially leading
to better predictions overall.
So it's like the model is thinking ahead,
trying to anticipate what's coming next.
That's smart.
So how much computing power do you need to train a model
that can do all this?
I mean, that's gotta be some serious hardware, right?
Oh, it definitely requires a lot of computational resources.
They used a massive compute cluster with 2048 NVIDIA H800 GPUs
to train DeepSeq v3.
2048 GPUs?
Wow, that's a lot of GPUs.
Do they need all of that?
Training a model of this scale, it
does require that immense computational power.
Yeah.
But they were smart about it.
They didn't just throw hardware at the problem.
They developed a really sophisticated training
framework to handle that kind of scale.
So it's not just about the hardware,
but it's also about being smart with how you use it.
Absolutely.
They used what's called a dual pipeline approach,
where they break the training process into chunks,
and they cleverly overlap the computation and communication
to maximize efficiency.
I see.
It's kind of like an assembly line,
where different parts of the model
are being built and tested simultaneously.
Yeah, like an assembly line, like that analogy.
But all that data being processed,
how do they manage the memory demands?
They had to get creative with memory saving techniques,
things like recomputation, storing things in the CPU,
really optimizing how they used all those resources.
OK, so we've got the efficient architecture with MLA
and DeepSeqMoh.
We've got the smart prediction with MTP,
and then we've got the massive compute cluster
with this really clever memory management.
What else did they do that's worth highlighting?
This is where it gets a little bit technical.
But they also use something called
FP8 mixed precision training.
FP8.
Now, that sounds a little intimidating.
Can you explain that in a way that even I can understand?
Of course.
Imagine you're baking a cake.
You could use a super precise scale
to measure every ingredient, but it would take forever.
FP8 is kind of like using a slightly less precise scale.
You might be off by a tiny bit, but it's much faster,
and the cake still turns out delicious.
Also, it's a trade-off between being super accurate
and being faster and more efficient.
Exactly.
And they were very clever about how they implemented it.
They carefully chose which operations were done in FP8
and which needed that higher precision,
making sure they didn't sacrifice too much accuracy.
I see.
But they still got a significant speed boost.
So they found that sweet spot.
Yeah, that's fascinating.
So training a model is just one part of the story, right?
You actually have to be able to use it.
How easy is it to actually deploy DeepSeq V3?
That's where DeepSeq V3 really shines.
They developed a smart deployment strategy
that involves two stages, prefilling and decoding.
Refilling and decoding.
OK.
So prefilling, I imagine that's like getting the model
ready for action.
And then decoding is where the magic happens,
where it actually generates text.
You got it.
OK.
And they've optimized each of these stages
for different needs, using techniques like tensor
parallelism, sequence parallelism, data parallelism,
and expert parallelism.
It's a pretty complex system, but the key takeaway
is they've put a lot of thought into making DeepSeq V3
as efficient as possible, both in training
and in how it's used.
Yeah, that makes sense.
So it sounds very impressive.
But with all this talk about efficiency and speed,
did they have any thoughts on how future hardware could
be designed to make AI even better?
You know that's one thing that really impressed me
about this paper.
OK.
They didn't just focus on the model itself.
They looked at the bigger picture.
Based on their experience with DeepSeq V3,
they actually have some specific suggestions
for future hardware design.
Like what kind of suggestions did they get into the details?
They did.
They talk about things like offloading certain communication
tasks to dedicated hardware to free up resources.
They talked about increasing the precision
of certain calculations.
I see.
And improving support for this FTA mix precision approach
we talked about.
It's clear they're thinking long term, not just
about this model, but about how to advance AI hardware
in general.
That's cool.
So they're not just building a model.
They're kind of pushing the whole field forward.
Yeah.
OK.
So we've talked about the architecture, the training
process, the deployment, even future hardware suggestions.
What about the data they use to train this thing?
Where did they get all that information from?
This is where the scale of this project really hits you.
They train DeepSeq V3 on a data set of 14.8 trillion tokens.
14.8 trillion?
That's mind boggling.
Where on earth do you find that much data?
They gather data from a huge variety of sources,
ensuring that DeepSeq V3 has a broad understanding
of different topics and writing styles.
And to train on this massive data set,
they used a strategy called Fill in the Middle or FEM.
Fill in the Middle or FEM.
Yeah.
I'm not familiar with that.
What's that?
So imagine you have a sentence with a missing word
in the middle.
OK.
The FEM strategy trains the model
to predict that missing word based
on the surrounding context.
So it's like a giant game of fill in the blanks.
It is.
OK.
Forcing the model to understand relationships
between words and concepts.
So it's not just understanding individual words,
but how they fit together in a bigger context.
OK, cool.
You mentioned earlier something about DeepSeq V3
being able to handle really long pieces of text.
How is that possible?
That's not something you see in every language model, right?
You're right.
They dedicated a whole section of the paper
to explaining how they extended its context window
to a whopping 128,000 tokens.
128,000 tokens.
That's like a small novel.
I know.
They used a technique called Yarn,
which allows the model to process much longer sequences
of text.
So that would be good for things like analyzing
long legal documents, maybe summarizing research papers,
even having a conversation that spans multiple pages.
Exactly.
OK.
Wow.
So we've covered the architecture, the training
process, the massive data set, even the long context
capability.
But how well does DeepSeq V3 actually perform?
Did they put it to the test?
They did.
And the results are quite impressive,
especially for an open source model.
They tested it on a wide range of benchmarks,
covering everything from code and math
to general language understanding and reasoning tasks.
So they really wanted to see how it stacked up
against the competition, both open source and closed source.
Exactly.
And on many of these benchmarks, DeepSeq V3 actually
outperformed other open source models,
especially when it came to those tasks involving code and math.
It even held its own against some of the leading closed
source models.
That's promising.
But I'm guessing the story doesn't end there.
I mean, they didn't just build this amazing model
and call it a day.
You're right.
The journey doesn't end with pre-training.
They took this powerful base model
and refined it further using techniques
like supervised fine tuning or SFT and reinforcement learning
or RL.
SFT and RL.
Can you unpack those a little bit?
I know they're common in AI, but a quick refresher
would be great.
Sure.
So think of SFT as giving the model a crash course
and how to be helpful and follow instructions.
OK.
They used a massive data set of instructions and examples
to fine tune DeepSeq V3 teaching it
to generate more human-like text and respond appropriately
to different prompts.
So it's like polishing a raw diamond.
You're taking something that's already powerful
and you're making it even more refined and useful.
What about RL?
How does that fit into the picture?
RL is where things get really interesting.
It's like giving the model a personal trainer,
helping it learn through trial and error.
Interesting.
So how does that work?
They use what are called reward models
to provide feedback to DeepSeq V3 during the RL process.
These reward models act as judges.
OK.
Evaluating the responses generated by the model
and giving it rewards for good responses and penalties
for bad ones.
So it's like the model is learning from its mistakes
and trying to improve its performance over time,
like a student getting grades on their homework.
Exactly.
And they used a really clever algorithm called GRPO
to make this RL process even more efficient.
The result is a fine-tuned model called DeepSeq V3
Chat, which is specifically designed
for interactive conversations and tasks.
DeepSeq V3 Chat.
OK.
And how well does it perform compared to that base model?
Did they see a significant improvement
after all this fine-tuning?
Oh, they did.
DeepSeq V3 Chat performs remarkably well, even
rivaling some of the leading close source models out there.
Really?
But you know what?
I think we should save the details of those evaluations
for the next part of our deep dive.
OK.
You're right.
We've covered a lot of ground already.
We have.
And there's still so much more to explore.
So stay tuned for part two of our deep dive
where we'll explore how DeepSeq V3 Chat performs
in different tasks, what its strengths and limitations are,
and what its potential impact could be on the AI field.
Sounds good.
Welcome back, everyone.
Before the break, we were talking
about that extensive fine-tuning process
that DeepSeq V3 went through.
Right.
We left off with you mentioning DeepSeq V3 Chat
and how well it performs.
So how did it do in those evaluations?
Spill the beans.
Well, they tested it on a whole new set of benchmarks.
And here's where it gets really interesting.
It really excels in areas like long-hawn text
understanding, coding, math, and even Chinese language tasks.
Yeah, for instance, on the DRUP-P benchmark,
which tests how well a model can understand and answer
questions from long passages of text,
DeepSeq V3 achieved a really impressive score.
OK, so it can handle those long, complex pieces of text
we talked about earlier.
So that's great for things like summarizing research papers
or analyzing legal documents.
What about its performance in coding and math?
Those are pretty important areas for AI these days.
Absolutely.
Encoding DeepSeq V3 really shines especially in tasks
that require that algorithmic thinking.
They tested it on benchmarks like HumanAvileMol and LiveCode
Bench, and it consistently outperformed other models.
I see.
This is partly thanks to that knowledge distillation
technique we discussed earlier, where they essentially
transfer knowledge from a more specialized coding model
into DeepSeq V3.
So it's like they gave it a crash course in coding
from a seasoned expert.
That makes sense.
What about math?
Can it solve those tricky word problems that used to give us
headaches back in school?
You bet.
They even tested it on problems from the Chinese National
High School Mathematics Olympiad.
And the results suggest that it can grasp those complex
mathematical concepts and solve problems that require
logical reasoning and multi-step calculations.
That's impressive.
They really put it through the wringer.
But a lot of these AI models are developed in English-speaking
countries.
How well does DeepSeq V3 actually perform in other languages,
like Chinese, for example?
That's a great question.
And it's something that they address in the paper.
They actually compared DeepSeq V3 to a leading Chinese language
model called QUIN.
And DeepSeq V3 held its own, showing impressive accuracy
and fluency in Chinese.
So that's encouraging to hear.
It's important for these AI models
to be accessible and useful for people around the world,
not just English speakers.
Definitely.
It speaks to the team's commitment
to making AI more inclusive and versatile.
Now, you mentioned earlier that DeepSeq V3 even rivals some
of the top closed-source models out there.
Did they do any head-to-head comparisons
to see how it stacks up?
They did.
And this is where things get really exciting.
They used a variety of benchmarks,
including some that are specifically designed
to test a model's ability to reason, be creative,
and even generate different writing styles.
So they weren't just looking at accuracy,
but also at things like creativity
and how human-like the text is.
That's really interesting.
And how did DeepSeq V3 fare in these more subjective evaluations?
It performed remarkably well, even exceeding expectations
on some of the tougher benchmarks.
For instance, on Arena Hard, a benchmark known
for its challenging prompts, it became
the first open-source model to achieve a win rate over 85%.
Over 85%.
That's a big deal.
It really shows that open-source models are catching up
to their closed-source counterparts.
Absolutely.
And here's another fascinating tidbit.
They even tested DeepSeq V3 as what's
called a generative reward model.
A reward model.
You mean they used DeepSeq V3 to judge the output
of other AI models?
Exactly.
Remember how we talked about those reward models being used
to train DeepSeq V3 during reinforcement learning?
Yeah.
Well, they found that DeepSeq V3 was surprisingly
good at evaluating the quality of text generated
by other models, even performing on par with models
like GPT-4O and Clawd 3.5 in some cases.
So it's like the student becoming the teacher.
That's really cool.
It sounds like DeepSeq V3 is a real all-rounder,
capable of not just generating text but also evaluating it.
But I'm curious, did the researchers uncover
any limitations or areas where the model could be improved?
That's a great question.
And your right no model is perfect.
One thing they highlighted was the size
of the recommended deployment unit.
It's still quite large.
OK.
Which could be a challenge for smaller teams
who might not have access to that level of computing power.
That makes sense.
It's a trade-off, right?
You get this amazing performance,
but it comes at the cost of needing
significant computational resources.
Exactly.
And while their deployment strategy
is much improved compared to previous versions,
they acknowledge that there's still room for improvement
in terms of speed and efficiency.
They're actively exploring ways to make DeepSeq V3 more
accessible and easier to deploy, especially
for real-time applications like chatbots.
So it's an ongoing journey.
There are still hurdles to overcome.
But even with these limitations,
it's clear that DeepSeq V3 is a significant achievement.
What do you think are the biggest takeaways from this research?
I think one of the key takeaways
is the sheer potential of open source AI.
DeepSeq V3 demonstrates that open source models
can be just as powerful and versatile as their close source
counterparts, if not more so in certain areas.
And the fact that the researchers have been so transparent
about their methods and findings is hugely
beneficial for the entire AI community.
Right.
It makes these powerful tools available to a wider
range of researchers, developers, and even hobbyists
who might not have the resources to access
those big proprietary models.
It really democratizes AI like we were talking about earlier.
Exactly.
And this leads to the second major implication.
With more people able to experiment with and build
upon these open source models, we're
likely to see an explosion of creativity and innovation
in the AI space.
It's exciting to think about all the potential applications
that could emerge.
And I imagine this will also lead to more robust and reliable AI
systems as researchers around the world
can collaborate, share their findings,
and work together to improve these models.
Absolutely.
Openness and transparency are crucial for building trust
in AI.
And DeepSeq V3 is a great example
of how we can move towards a more open and collaborative
future for this technology.
But there's another implication that I think
is particularly important.
And it relates to the paper's title,
Scaling Open Source Language Models with Long Termism.
OK, long termism.
That word definitely got my attention
when we first introduced the paper.
Can you elaborate on what the researchers mean by that
and why it's important?
They're emphasizing the need to think
beyond just the immediate capabilities of these models.
OK.
They argue that we need to consider
the long term ethical and societal implications of developing
and deploying increasingly powerful AI systems.
That's a crucial point.
It's not just about building bigger and better models.
It's about ensuring that these advancements benefit
humanity as a whole and that we're
prepared for the challenges that might arise as AI becomes
more integrated into our lives.
Exactly.
And I think the researchers behind DeepSeq V3
are setting a great example by not just focusing
on technical achievements, but also
by being mindful of the broader impact of their work
and advocating for a more responsible and inclusive
approach to AI development.
Well said.
It's a reminder that AI is not just a technological pursuit.
It's also a human one.
And we need to ensure that these advancements align
with our values and contribute to a better future for everyone.
So where we go from here?
What's next for DeepSeq V3 and for the open source AI
landscape in general?
That's a great question to ponder
as we move to our final part.
Join us for a short break as we delve into the future
of this exciting field.
We're back.
And I'm still thinking about all this stuff about DeepSeq V3.
It's amazing to see how far open source AI has come.
Yeah, it's really remarkable.
And this is just the beginning.
The DeepSeq team has already hinted at future versions
that promise even more capabilities and efficiency.
Really?
Yeah.
What will these future models be able to do?
What kind of problems will they solve?
Well, I think we can look at the trajectory of AI development
to get some clues.
We're seeing these models become increasingly
adept at understanding and generating human language,
which opens up a whole world of possibilities.
Like what?
Give me some examples.
OK, so imagine personalized tutors powered
by DeepSeq tailoring lessons to each student's individual needs
and learning styles.
OK, yeah.
Or imagine AI assistants that can help us write more
effectively, translate languages seamlessly,
or even generate creative content like stories, poems,
or scripts.
It's like having a super-powered brainstorming partner
who can just help you unlock your creative potential.
And I bet there are huge implications
for fields like scientific research and health care, too.
Oh, absolutely.
Imagine DeepSeq being used to analyze vast amounts
of scientific data, helping researchers make new discoveries
and breakthroughs.
Or imagine it being used to develop more personalized medical
treatments tailored to each patient's unique genetic makeup
and health history.
Yeah, the possibilities were pre-mind-boggling.
It's really exciting to think about how this technology could
be used to improve so many aspects of our lives.
But with all this talk about potential and possibilities,
it's important to remember that we need to develop and deploy
these technologies responsibly.
Yeah, for sure.
We need to make sure AI is used for good
and that it benefits humanity as a whole.
Another DeepSeq team has emphasized that long-termism
approach, thinking about the long-term ethical and societal
implications of AI.
But what can we as individuals do
to ensure a positive future for AI?
I think one of the most important things we can do
is stay informed and engaged in the conversation about AI.
We need to educate ourselves about the potential benefits
and risks of this technology and participate in discussions
about how it should be developed and used.
So it's not just about the researchers and developers.
It's about all of us taking an active role
in shaping the future of AI.
What are some practical steps people
can take to get more involved?
Well, there are many ways to get involved.
You can read articles and books about AI,
attend conferences and workshops, or even join online communities
where people are discussing these topics.
And don't be afraid to ask questions, challenge assumptions,
and voice your concerns.
It's through open and honest dialogue
that we can ensure AI is developed and used
in a way that aligns with our values.
Well said.
It's a reminder that the future of AI is not predetermined.
It's something that we're actively shaping
through our choices and our actions.
And that's both a responsibility and an incredible opportunity.
Definitely.
As we learn more about these powerful AI models,
like DeepSeq V3, it's up to all of us
to ensure that we're using them wisely and ethically.
OK, before we wrap up, I want to go back
to the DeepSeq V3 paper itself.
What's your final takeaway for our listeners?
What do you hope they'll remember from this deep dive?
I hope they'll remember that the world of open source AI
is vibrant and innovative and full of potential founder.
DeepSeq V3 is a testament to what's
possible when researchers collaborate, share their knowledge,
and push the boundaries of this technology.
And I hope that we inspire to learn more
to explore these ideas further and contribute
to the conversation about how we can shape the future of AI
in a positive and beneficial way.
So to our listeners, we encourage you to check out
the full DeepSeq V3 paper.
It's a fascinating read, even if you're not a technical expert.
And as you explore the paper, think about the questions we've
raised today.
What excites you about the potential of AI?
What concerns do you have?
And most importantly, how can you be a part of shaping a future
where AI is used for good?
Thanks for joining us on this deep dive into DeepSeq V3.
It's been an insightful journey.
And we look forward to exploring more fascinating corners
of the AI world with you in our next episode.
Until then, keep learning, keep questioning,
and keep imagining the possibilities.