Welcome back to the AI Papers podcast daily.
You're not going to believe this,
but we just got our hands on the Quinn 2.5 technical report.
Oh, wow.
Hot off the presses, release just today, December 20th, 2024.
No way.
And let me tell you, the Quinn team
is really pushing the boundaries of what's
possible with large language models.
Yeah, they really are.
I mean, this report is packed with details
about their latest series of models.
And some of the advancements are truly remarkable,
especially when it comes to context length
and how they've tackled the data challenges.
Absolutely.
OK, let's unpack this.
Sounds good.
First things first, Quinn 2.5 isn't just one model.
It's a whole family of them, right?
Exactly, yeah.
They've developed models of various sizes, both open weight,
meaning they're freely available for anyone to use,
and proprietary, which are accessible through Alibaba Cloud.
So for all the AI enthusiasts out there,
whether you're a researcher, developer, or just someone who
loves to tinker with the latest AI tools, you've got options.
We're looking at sizes from a nimble 0.5 billion parameters,
all the way up to a massive 72 billion parameter model
in the open weight category.
That's incredible, yeah.
And for those who need even more horsepower,
there's Quinn 2.5 Turbo and Quinn 2.5 Plus,
which are the proprietary models.
These are built using a mixture of experts approach,
interesting, which we can dive into a little later.
Yeah, I'm really excited to see the return of the 3B, 14B,
and 32B models.
Yeah.
It's great that they're offering a range of sizes
to fit different needs and resources.
It's a smart move.
You know, not everyone has access
to massive computing clusters.
Right.
So having these mid-range options
makes this cutting-edge technology much more accessible.
It's a wider range of users.
Now, let's talk about what they fed this beast.
OK.
I was blown away by the sheer scale of the training data,
18 trillion tokens.
Oh.
That's more than double the previous version.
That's a lot of data.
But what's really interesting is how they went beyond just size.
You know?
They used their previous models to filter the data for quality.
So it's not just about quantity.
It's about making sure every token counts.
So they're using their own AI to train even better AI.
Exactly.
That makes sense, though.
Yeah.
If you want your model to be a math whiz or a coding guru,
you got to make sure it's got the right stuff
and it's learning diet.
Right.
It's like cross-training for AI.
Exactly.
They also went a step further and generated synthetic data
specifically for math, code, and general knowledge domains,
all using their previous models to ensure quality.
So they're really trying to cover all their bases.
They are.
And it sounds like they were very aware
of the potential for bias in their data,
especially from sources like social media.
Absolutely.
Yeah.
They strategically balance the content
across different domains, downsampling
overrepresented areas like social media,
and up-sampling valuable areas like tech and science.
This helps create a more balanced training set
and a more knowledgeable model.
This meticulous approach to data quality
really sets them apart.
It's not just about chasing bigger numbers.
It's about building models that are both capable and reliable.
Right.
OK.
So we've covered the data feast.
The feast, yeah.
Now let's talk about the architecture
they use to build these models.
OK.
Sounds complex.
So for the dense models, they stuck with a transformer-based
decoder architecture, similar to the previous Quintu.
Gotcha.
But with some key enhancements.
Like what?
Well, they've used some interesting tricks in the architecture,
like this thing called grouped query attention.
What's that?
It basically helps the model use memory more efficiently.
Which is a big deal when you're dealing
with massive amounts of data.
That's a mouthful.
It is.
But I get it.
Yeah.
Efficiency is key when you're working
with these massive models.
Definitely.
It's like optimizing your code so it runs faster.
Right.
Exactly.
And they've also incorporated other features,
like a switchy-LU activation function and rotary
positional embeddings or row B.
Wow.
OK.
All of which are designed to improve the model's performance.
I'm sensing a theme here.
Yeah.
Squeeze every bit of performance out of these models.
It seems so.
So we've got the data.
Uh-huh.
We've got the blue trends for the model.
OK.
Now let's talk about the training process.
Sounds good.
It's a two-phase approach.
Right.
OK.
Walk me through it.
So phase one focuses on pre-training
with a context length of 4,096 tokens.
OK.
Think of it as building the foundation,
getting the model familiar with the basics of language
and how words relate to each other.
So that's like teaching the model the alphabet
and basic grammar.
Exactly, yeah.
And what happens in phase two?
In phase two, they extend that context length
for longer sequence training.
OK.
Up to 32,768 tokens for most models.
Hold on.
32,768 tokens.
How much information is that?
It's a lot.
Think of it as feeding the model an entire research paper
or a long-form article.
Oh, wow.
But there's one model, Quen 2.5 Turbo,
that gets its own special training regimen.
Special treatment?
Yeah, they gradually expand the context length,
ultimately reaching a mind-blowing 262,144 tokens.
262,144 tokens.
That's, I don't even know what to compare that to.
It's a lot.
That is a lot.
How does a model even handle that much information?
That's a great question.
Yeah.
They implemented some clever techniques,
like yarn and dual-chunk attention or DCA.
OK.
These innovations not only help the model
understand long contexts, but also maintain its performance
on shorter ones.
So it's not just about cramming in as much information
as possible.
It's about making sure the model can actually
use it effectively.
Precisely.
Yeah.
And you know what's even more amazing?
They figured out a way to make Quen 2.5 Turbo
handle up to 1 million tokens during inference,
which is when the model is actually using what
it's learned to perform a task.
1 million.
That's astronomical.
It is.
I can't even wrap my head around that.
Yeah.
What are the practical implications
of this kind of long context processing power?
Well, imagine you're a researcher digging
through mountains of historical documents.
Quen 2.5 can analyze all that, find connections
you wouldn't believe.
Wow.
And even summarize it for you in plain English.
That's the power of a million token context length.
Exactly.
That's incredible.
I'm starting to see how this technology could
revolutionize all sorts of fields
from scientific research to creative writing.
Yeah.
And this is where those mixture of experts models,
or MOE for short, really come into play.
They're designed for handling this kind of massive scale.
OK.
Remind me again, how do these MOE models work?
I know we touched on it earlier.
But I want to make sure I understand it fully.
OK.
So imagine instead of one massive network trying
to do everything, you have multiple smaller networks,
each specializing in a specific task.
Select specialists.
Exactly.
These specialists are called experts.
That's it.
So when a new task comes along, only the experts
whose skills are needed get activated.
Oh, so it's like having a team of specialists.
Exactly.
Instead of one generalist trying to do everything,
that sounds much more efficient.
Exactly.
This specialization makes MOE models incredibly efficient.
Makes sense.
Especially when dealing with huge amounts of data
and complex tasks.
So we've covered the data, the architecture,
and the pre-training.
OK.
But the journey doesn't end there.
No, it doesn't.
Let's talk about post-training.
All right.
Where the Quen team really fine-tuned these models
for specific skills and behaviors.
That's right.
That's when things get really interesting.
This is where things get really interesting.
Absolutely.
Yeah.
Post-training is like sending your LLM to grad school.
OK.
It's where they take that raw potential
and shape it into a model.
OK.
That can follow instructions, understand complex concepts,
and generate human quality text.
Gotcha.
And Quen 2.5's post-training regimen
involved two major advancements.
OK.
Expanded supervised fine-tuning data coverage.
And a two-stage reinforcement learning process.
OK.
Let's break those down.
That was good.
First up, expanded supervised fine-tuning.
What's new and exciting here?
Well, they used a massive data set
with millions of high-quality examples
to address limitations they observed in previous models.
So what kind of limitations?
Well, they wanted to refine the models in areas
like long sequence generation, math, coding, and following
instructions flawlessly.
Really?
Nail those down.
Yeah.
It's all about creating a model that's not just smart,
but can actually put that smarts to work in a useful way.
It's like taking a brilliant student
and teaching them how to apply their knowledge
to solve real-world problems.
I like it.
And they didn't stop there.
OK.
They also implemented a two-stage reinforcement learning
process, which is all about training models
through a system of rewards and penalties.
Kind of like teaching a dog new tricks with treats
and corrections.
Exactly.
The first stage, offline RL, focused
on developing capabilities like reasoning and factuality.
OK.
Makes sense.
Yeah.
It's like teaching the model to solve a complex puzzle.
Right.
Where the solution isn't immediately obvious.
Exactly.
So what happens in the second stage?
Online RO, they refined the model's ability
to produce responses that are truthful, helpful, concise,
relevant, and harmless.
So it's like taking those raw reasoning skills
and polishing them into a communication style
that's both effective and aligned with human value.
Right.
They're shaping not just the model's intelligence,
but its personality as well.
That's a great way to put it.
Now the big question is, did all this careful training
pay off?
That is the question.
Let's get to the results.
OK.
How do these instruction tuned models perform?
I'm ready to hear about some AI magic.
All right.
So the results were impressive across the board.
They used a mix of open benchmarks, their own in-house
data sets, and even assessments of their long context
capabilities.
OK.
Especially for those models designed
to handle massive amounts of information.
OK.
Let's start with those open benchmarks.
These are the standardized tests that
allow us to compare performance across different LLMs,
right?
Yeah.
Like the SATs for AI.
Exactly.
OK.
How did Quen 2.5 do on its exams?
They passed with flying colors.
Right?
Starting with the 72B parameter model,
Quen 2.572BB consistently outperformed its peers
in the same size category across a wide range of tasks.
Wow.
It even achieved results comparable to the much larger
LLAMA3405B.
That's a big one.
Which has five times the parameters.
Wow.
That's a huge accomplishment.
So they're achieving state of the art performance
with a model that's significantly smaller.
Right.
I bet that has implications for making
these models more accessible.
Absolutely.
Yeah.
And they weren't content with just matching performance.
They also wanted to beat their own previous best.
You're talking about the Quen 272B model, right?
The one they released before this new series?
That's the one.
OK.
And Quen 2.572B showed marked improvements
over its predecessor in almost all the benchmark evaluations.
Really?
In what areas?
Especially in general, tasks, math, and coding.
I'm sensing a theme here.
Yeah.
Math and coding seem to be areas where Quen 2.5 really shines.
Definitely.
It's a strength.
Yeah.
They clearly focused on building models
that can go beyond simply understanding text
to actually applying that understanding
in practical and challenging ways.
That's really impressive.
It is.
OK.
What about the proprietary Moe models?
Right.
How did they fare?
Exceptionally well.
Really?
Especially considering their lower training and inference
costs compared to the dense models.
Wow.
Quen 2.5 Plus delivered very competitive results
compared to both Quen 2.572B and Lama 3405B.
So those are the biggest models?
Yeah.
OK.
It even outperformed other models
on several challenging benchmarks.
So they're not just powerful.
They're also efficient.
That's a winning combination.
It is.
Let's talk about some of the smaller models now.
Like the 14B and 32B models.
Right.
Where Quen 2.5 Turbo also fits in.
OK.
How do they stack up?
Even at these smaller sizes, Quen 2.5 continued to impress.
OK.
The 14B model delivered solid performance across the board,
even outperforming some larger models in certain areas.
And the 32B model.
What stood out there?
The 32B model was a real standout,
often exceeding the performance of larger models
with similar architectures.
Wow.
Particularly in math and coding tasks.
There's that math and coding prowess again.
Right.
Now, let's bring in Quen 2.5 Turbo in this size range.
OK.
How did it stack up?
It managed to achieve comparable results to the 14B model.
Wow.
Even though its training and inference costs
were significantly lower.
That's impressive.
This efficiency is really remarkable,
especially when you consider that they managed
to maintain performance.
It makes me wonder what they could achieve
if they applied this moe approach to even larger models.
The possibilities are exciting, aren't they?
It's like they've found a secret formula.
Right.
All right, now let's shift gears and talk about the 7B models.
OK.
How did they measure up?
They compared Quen 2.5 7B with Mistral 7B,
Llama 38B, Gemma 2.9 B.
So some other 7Bs.
Yep.
And their own previous model, Quen 2.7 B.
So a mix of the top contenders and their own previous work.
Exactly.
Which is a great way to track progress.
It is.
And how did it perform?
The results were very positive, despite having slightly
fewer parameters than some of the other models.
Quen 2.5 7B surpassed both its predecessors and competitors
on numerous benchmarks.
What were some of the standout wins for Quen 2.5 7B?
Anything particularly impressive?
Yeah, it demonstrated significant improvements
across a variety of tasks.
OK.
From general benchmarks like MMLU to math challenges
like math and coding tasks like Human Evil.
So it did really well on the coding and math.
It did.
So even at this smaller scale, they're still holding their own.
Absolutely, yeah.
OK.
And then we have the smallest models designed for things
like your smartphone and laptop.
Right.
Quen 2.5 0.5 B, 1.5 B, and 3B.
OK.
So these are the smallest models in the set.
Yeah.
And despite their compact size, these models
held their own against established baselines.
Really?
In fact, Quen 2.5 0.5 B even outperformed Gemma 2 2.6 B
on several math and coding tasks.
Oh, wow.
So even the smallest ones are still very capable.
They are.
That's pretty incredible.
He is.
It's like they miniaturized the power of an LLM
without sacrificing his core capability.
Yeah.
What are the implications of being
able to run these powerful models on everyday devices?
That's a great question.
It really opens up the possibilities for using LLMs
in new and exciting ways, bringing the power of AI
to everyone.
Yeah.
Think about personalized AI tutors.
Oh, wow.
That can fit in your pocket.
That's amazing.
The future of AI is looking pretty bright.
It is.
Before we move on to the next part of this deep dive,
I'd like to understand the MOE models a little better.
All right.
I get the basic idea.
Yeah.
Having specialized experts instead of one generalist.
But how does this actually work in practice?
It's like having a team of chefs in a restaurant, each
with their own specialty, instead of one chef
trying to cook everything.
Oh, what?
You have specialists for pasta, desserts, grilling.
I like where you're going with this.
And so on.
OK.
This makes the kitchen run much more efficiently.
OK.
That's a great analogy.
So in a MOE model, instead of one giant network,
you have these specialized experts.
Right.
But how does the model know which expert
to call on for a given task?
That's where the routing mechanism comes in.
What cat?
It's like the head chef directing traffic in the kitchen,
making sure the right cooks are working on the right orders.
So it analyzes the input data and decides
which experts are most likely to provide the best output.
Right.
It's like having a smart manager.
Yeah.
Who knows exactly which team member
has the right skills for the job?
Exactly.
OK.
This routing mechanism is what makes MOE models so efficient.
Gotcha.
They're only using the brain power they need for a specific task.
I see why the Quinn team chose to use MOE models
for their largest and most powerful offerings.
Yeah.
It's a really clever way to handle the challenges
of scale and complexity.
It is.
But did these MOE models live up to the hype?
They did.
Both Quinn 2.5 Turbo and Quinn 2.5 Plus
delivered exceptional results, often exceeding
the performance of larger dense model.
So better than the non-MOE model.
Yep.
While using significantly less computational power.
So they're not just powerful.
They're also incredibly efficient.
Right.
It's like getting the same amazing meal.
But the kitchen is running like a well-oiled machine.
And that's what makes the Quinn 2.5 research so exciting.
I agree.
They're achieving high performance in a way that's
actually practical and accessible.
I can't wait to hear more about these MOE models
as they evolve.
Neither.
This feels like a major turning point in AI development.
Yeah.
Now, I think we've laid a solid foundation
with our discussion of the base models and the MOE approach.
OK.
Are you ready to move on to the next part of the deep dive?
I'm ready.
Those instruction tuned models.
Absolutely.
This is where we see these base models transformed
into truly capable and responsive AI assistants.
I'm already to be impressed.
All right.
Let's do it.
All right.
Let's dive into the world of instruction tuned models.
OK.
What makes these models different from the base models
we discussed earlier?
Think of the base models as having raw talent.
OK.
Like a promising athlete who hasn't yet
received formal coaching.
I see where you're going with this.
Yeah.
They have the potential to do incredible things.
But they haven't been specifically
trained to follow instructions, understand
complex nuances of language, or consistently
generate human quality text.
Gotcha.
Instruction tuning is like giving that athlete expert
coaching, honing their natural abilities
into finely tuned skills.
So it's like taking a brilliant student
and teaching them how to apply their knowledge
in the real world, solving practical problems.
Exactly.
And in the case of Quen 2.5, they took their instruction
tuning to a whole new level.
This is where those two key advancements come in, right?
The expanded, supervised fine tuning data
and that two-stage reinforcement learning process.
You got it.
Yeah, those advancements were crucial
for creating the impressive instruction tuned models
in the Quen 2.5 series.
Cool.
They supercharged their training data
with millions of high quality examples.
Wow.
Targeting areas where previous models struggled.
So areas like generating long, coherent responses,
solving complex math problems, writing code,
and following instructions flawlessly.
Precisely, yeah.
They wanted to make sure the model had seen and learned
from as many different types of instructions
and desired outputs as possible.
So they really tried to cover all the bases.
They did.
Almost like creating a training simulation
for a real world AI assistant.
It sounds like they were trying to anticipate
the kinds of tasks these models might encounter
in real world applications and train them accordingly.
Exactly.
And the two-stage reinforcement learning process
was key for fine tuning the model's ability
to not only understand instructions,
but also to generate responses that aligned with human
preferences and values.
I remember you mentioned earlier that offline RL focused
on developing capabilities like reasoning and factuality,
which can be tricky for a reward model to evaluate directly.
Yeah, it's like teaching the model
to solve a complex puzzle, where the solution isn't obvious.
They used clever techniques like answer matching
and execution feedback along with human review
to make sure the model was learning the right lessons.
So it's more nuanced than that.
Even when the reward signals were subtle.
Exactly.
So it's not just about giving the model a pat on the back
when it gets the answer right.
It's also about guiding it towards developing
a deeper understanding of the task at hand.
Then with online RL, they refined the model's output
for more nuanced qualities like truthfulness,
helpfulness, conciseness, relevance, and harmlessness.
Exactly.
So it's like taking those raw reasoning skills
and polishing them into a communication style that's
both effective and aligned with human values.
Yeah.
They're shaping not just the model's intelligence,
but its personality as well.
That's a great way to put it.
Now the big question is, did all this careful training pay off?
Let's get to the results.
Yeah.
How did these instruction-tuned models perform?
I'm ready to hear about some AI magic.
The results were impressive across the board.
OK, in what ways?
Well, they used a mix of open benchmarks,
their own in-house data sets, and even assessments
of their long context capabilities,
especially for those models designed
to handle massive amounts of information.
OK, let's start with those open benchmarks.
These are the standardized tests that
allow us to compare performance across different LLMs,
right, like the SATs for AI.
Exactly.
And in these standardized tests, Quen 2.5
held its own against some of the top models in the field.
OK, that's good.
In fact, their flagship model, Quen 2.5 72B Instruct,
delivered exceptional performance,
even surpassing the much larger Lama 3.1 405B Instruct
on several key benchmarks.
So they beat the big guys.
They did.
Wow, that's a huge accomplishment.
So they're achieving state-of-the-art performance
with a smaller, more efficient model.
Right.
That's a big deal.
It is.
And it wasn't just the largest model that impressed.
OK.
Quen 2.5 Plus.
So one of the Moe models.
Yeah, their Moe model also outperformed Quen 2.5 72B
Instruct on several benchmarks.
So even better in some cases.
Yeah, further demonstrating the power and efficiency
of the meta approach.
I'm starting to see why you're so excited about Moe models.
They seem to be consistently punching above their weight.
They really are.
Now, moving down to the 14B and 32B instruction tuned models.
OK.
Where Quen 2.5 Turbo also comes into play.
Right.
They compared their models with some heavy hitters,
like GPT-4 Mini and Gemma 2 27B ITT.
Yep.
So a mix of proprietary and open weight models.
OK.
All known for their strong performance.
And how did they do?
The Quen 2.5 models proved to be strong contenders
in this arena.
OK.
The 32B model in particular stood out
with impressive capabilities in math and coding,
consistently ranking among the top performers.
There's that theme again.
Quen 2.5 consistently excels at tasks
that require more than just basic language understanding.
All right.
They're really good at logic, reasoning, and problem solving.
Yeah, it's like they have a knack for figuring things out.
Exactly.
And how about Quen 2.5 14B Instruct?
Did it keep up with the big guys?
It definitely held its own.
OK.
Delivering competitive results across the benchmarks,
improving to be a strong contender against even the proprietary GPT-40
mini model.
So it did pretty well.
It did.
And of course, we can't forget about Quen 2.5 Turbo.
Right.
It continued to impress, even outperforming Quen 2.5 14B
Instruct on several benchmarks.
Really?
Despite its lower training and inference costs.
I'm telling you, these MOI models are going to be game changers.
I think so too.
Now for the 7B instruction tuned models.
OK.
They focused on comparing Quen 2.5 7B
with two leading open weight models, Gemma29BIT and Lama 3.1 8B
Instruct.
So how did Quen 2.5 7B stack up against those established competitors?
It significantly outperformed both of them on almost every task.
Really?
This really highlights the effectiveness of their approach
to pre-training, fine tuning, and data selection.
It's like they've figured out the secret sauce for training highly
capable LLMs.
It seems so.
It's not just about building bigger models.
It's about training them smarter.
And finally, they evaluated their smallest instruction tuned
models, the ones designed for edge side applications.
OK.
Like Quen 2.5 0.5B, 1.5B, and 3B.
OK, so like for smart funds.
Yeah.
These compact models showed substantial improvements
over their previous versions, making them ideal for applications
with limited computational resources.
That makes sense.
Yeah.
It's amazing to see how they're packing so much power
into smaller and smaller models.
Right.
It really democratizes access to AI,
bringing its potential to a wider audience.
Absolutely.
They've achieved a great balance between performance and accessibility.
They have.
So these open benchmarks paint a very positive picture of Quen 2.5's
capabilities.
Yeah.
But you mentioned earlier that they went beyond these standard tests.
Right.
Why was that?
They recognized that open benchmarks, while valuable,
don't always capture the full range of skills and behaviors
needed for a truly helpful and reliable AI assistant.
OK.
In the real world.
Yeah, that makes sense.
They wanted to put their models through more realistic tests.
So they created their own custom exams.
Exactly.
They created in-house data sets to assess their model's performance
in areas like knowledge understanding, text generation,
and coding.
All crucial skills for real world tasks.
And how did those go?
Well, they didn't just stick to English.
OK.
They conducted these evaluations in both English and Chinese.
So they're really serious about making their models work well
across different languages and cultures.
They are.
It's like they're training them to be global citizens.
I like that analogy.
And in their in-house evaluations,
they compared their models with leading language models.
So the competition.
Yeah, including giants like GPT-4, Cloud 3.5 Sonnet, Quen 2,
and Lama 3.1.
Oh, wow.
So the heavy hitters in the LLM world?
Yep, across both English and Chinese.
OK, so how did Quen 2.5 measure up against these AI titans?
They more than held their own.
They often matched or exceeded the performance
of these larger and more established models,
even in very challenging areas.
That's impressive.
Can you give me some specific examples?
Yeah, in English, Quen 2.5.7 to B really stood out.
Matching or exceeding the performance of LLMA 3.1.4 or 5B
in almost every metric.
Wow, that's impressive.
And in Chinese, Quen 2.5 plus addressed
some previous limitations and instruction
following while maintaining its already strong performance
in other areas.
It's inspiring to see a newer model like Quen 2.5 competing
at such a high level against the giants of the field.
It is.
It's a clear sign of how quickly this field is advancing.
New approaches to training and architecture
are leading to rapid improvements.
It seems like every week there's something new coming out.
There is.
We're witnessing an AI revolution.
OK, let's talk about their multilingual capabilities.
I know we touched on it briefly.
But how do they really assess how well these models perform
across different languages?
They took a multifaceted approach.
They expanded existing benchmarks
to include multilingual examples and used
several MMLU-like benchmarks in various languages,
including Arabic, Japanese, Korean, Indonesian, and Turkish.
Wow, a lot of languages.
Yeah, they even tested the models on translated versions
of benchmarks, ensuring consistency across different language
versions.
Wow, they really went all out.
It sounds like they're committed to building
AI that can truly communicate across language barriers.
They are.
What were the results of these tests?
The results were encouraging.
Quen 2.5 consistently performed well
in instruction following, multilingual knowledge
understanding, and even math reasoning
across various languages.
So they're not just claiming to be multilingual.
They're proving it with hard data.
Exactly.
They even want it step further, testing the model's ability
to understand subtle cultural nuances.
Oh, wow.
It's one thing to translate words,
but it's quite another to grasp the cultural context.
That's really fascinating.
They're not just building language models.
They're building cultural interpreters.
Now, before we move on to their impressive long context
capabilities, we need to discuss one more piece
of the evaluation puzzle.
The reward model.
Oh, right.
The reward model.
This plays a key role in the reinforcement learning
process, right?
Exactly.
The reward model is like a coach.
Giving feedback to the LLM and guiding it towards generating
better responses.
But evaluating a reward model is different than evaluating
the LLM itself, isn't it?
You're absolutely right.
You can't just give it a test and grade it.
Instead, you have to assess how well it
predicts which responses humans would find most helpful,
truthful, and so on.
So how did they evaluate the reward model for Qwenn 2.5?
They used specialized benchmarks designed
for assessing reward models, including their own Chinese
human preference benchmark.
Gotcha.
It's a complex process, but essential for ensuring
that the reward model is actually doing its job.
And how did the Qwenn 2.5 reward model perform?
It performed well overall, even surpassing some established
reward models on certain benchmarks.
Really?
For example, it was the top performer in both the PPE
and human preference Chinese evaluations.
So it sounds like they did a good job with that, too.
They did.
This is a testament to their efforts
in developing a robust reward model.
It's amazing to see how much effort
they put into refining every aspect of the training process.
It's clear they're not cutting any corners.
They're really striving for excellence,
and that's reflected in their results.
However, they made a very insightful observation
during their evaluation.
What's that?
They found that simply achieving a high score
on a reward model benchmark doesn't always
mean that the LLM will also perform well.
That's interesting.
Yeah.
So there's a gap between how we currently evaluate reward
models and how effective they are in practice.
So there's still room for improvement there.
Exactly.
This highlights a need for better evaluation methods
that can more accurately predict real world performance.
Yeah, if that makes sense.
It's an area where more research is needed.
It's a reminder that even as we're making great strides in AI,
there are still fundamental challenges to address.
Yeah.
It's a constantly evolving field.
Absolutely.
Now, are you ready to move on to those long context
capabilities?
Let's do it.
This is where things get really mind blowing.
All right, they use benchmarks like Ruler, LVEvol,
and Long Bench Chat, which are specifically
designed to assess a model's ability
to handle long pieces of text.
What are some of the key skills needed
for handling long contexts?
What were they looking for in these evaluations?
Well, first and foremost, the model
needs to have a good memory.
OK.
It needs to remember information from earlier parts
of a long text.
Kind of like reading a novel and recalling details
from the first chapter when you're halfway through the book.
That's great analogy.
It's one thing to process a few sentences.
Right.
It's quite another to maintain understanding over thousands
or even millions of tokens.
Exactly.
They were also looking for the model's ability
to see the big picture, to understand the relationships
and connections between different parts of a long text.
So not just remembering isolated facts,
but actually grasping the narrative or argument,
the flow of ideas.
Precisely.
And Quen 2.5 delivered impressive results.
OK, that's good.
Especially the larger models like Quen 2.5.72B instruct.
So the big ones did well.
They did.
They even surpassed existing open weight and proprietary models
in their long context performance.
So they're not just handling long contexts.
They're excelling a task that require a deep understanding
of those contexts.
Exactly.
And they didn't just test their ability
to understand long texts.
OK.
They also tested their ability to generate them.
They gave Quen 2.5 Turbo a task called PASCII Retrieval,
where it had to find specific information hidden
within a million token document.
Wait, a million tokens?
That sounds impossible, like finding
a needle in a haystack of words.
It's a real challenge.
But it is.
But Quen 2.5 Turbo aced it.
Really?
Achieving 100% accuracy in retrieving
that hidden information.
Wow, that's impressive.
It's a testament to the model's ability
to not only process, but also effectively use
vast amounts of data.
That opens up incredible possibilities.
Imagine researchers, analysts, or writers
being able to instantly analyze and extract
key information from massive amounts of text.
Yeah.
It's like having an army of research assistants
at your fingertips.
Exactly.
And they also addressed the computational cost
of handling these long contexts.
They implemented a sparse attention mechanism,
which significantly speeds up the processing.
So they found a way to make it faster and more efficient.
They did.
That's great news for anyone who wants to use these models.
Yeah, it means more people can benefit
from these powerful capabilities without needing
access to specialized hardware or massive computing budgets.
I love how the Q and Team focuses on both performance
and accessibility.
I agree.
They're pushing the boundaries of AI
while making sure these advancements are
available to a wider audience.
I agree.
Their work is a great example of how AI research should
be done with a focus on both innovation and social impact.
Before we wrap up this deep dive,
I want to make sure we're presenting a balanced perspective.
Every groundbreaking development comes
with potential limitations.
It does.
And I want to make sure we cover those as well.
That's a great point.
So what are some of the limitations you noticed
in the QN 2.5 research?
Well, while their efforts to ensure data quality
are commendable, they acknowledge that biases can still
find their way into data sets, even with careful curation.
So bias is still a problem, even in these huge models.
Yeah, it's an ongoing challenge in the field of AI.
That's true.
Eliminating bias completely is a tough nut to crack.
What about the Moe models?
Do they mention any limitations there?
Well, while Moe models are very promising,
there's still a lot of ongoing research
into how to best design and train them.
There's no one-size-fits-all solution.
And finding the optimal configuration for a given task
can be complex.
Yeah, it's like a new tool with incredible potential.
But we're still learning how to use it most effectively.
Exactly.
And they also highlighted the need for better evaluation
methods for reward models.
Remember that observation about benchmark scores not always
being predictive of real-world performance?
I do.
Yeah, this suggests that we need to refine our evaluation
techniques to better assess how these models will actually
perform in practice.
It's like we need to find new ways
to measure not just how intelligent an AI is,
but how well it can actually apply that intelligence
to real-world situations.
Precisely.
And finally, even with their impressive long context
capabilities, they acknowledge that there's
a gap between what these models can do
and what humans can do when it comes to processing information
over extended periods.
It's good to be reminded that we're still
in the early days of AI development.
It is.
We've come a long way, but there's still a lot of room
to grow.
Absolutely.
But these limitations shouldn't overshadow
the incredible progress demonstrated in the Quen 2.5
research.
Right.
They've clearly addressed many of the challenges facing
LLMs.
Yeah, I care.
And their work is a major contribution to the field.
I'm excited to see how this research inspires further
innovation and leads to the development of even more
capable and helpful AI systems.
I agree.
What do you see as the key takeaways from this research?
What does it mean for the AI community and the world beyond?
Well, I think the most important takeaway
is that open-weight LLMs are catching up
to the performance of proprietary models.
OK.
The fact that Quen 2.5.7.2 be instruct
can compete head-to-head with models like LLAMA 3.1405B
instruct is a huge victory for open access and collaboration
in AI.
That's an important point.
It is.
Openness fosters innovation and accelerates progress.
Absolutely.
What other key takeaways did you notice?
I was also very impressed by the multilingual capabilities
of Quen 2.5.
Me too.
It seems they've really focused on training these models
to work well in many different languages.
It's like they're creating AI that
can speak to the whole world.
Yeah, and not just speak, but understand different cultures
and nuances.
Exactly.
It's a step towards breaking down language barriers
and fostering global communication.
It really is.
I'm curious to see how this kind of technology will impact
fields like translation, diplomacy,
and international collaboration.
It's exciting to think about.
I can imagine a future where people from different countries
can seamlessly communicate with each other,
regardless of their native language.
It's a beautiful vision, and I think this research is bringing
us closer to that reality.
I agree.
Their focus on efficiency is another crucial contribution.
Definitely.
They're showing that it's possible to achieve high performance
without relying on massive computing clusters.
Right.
This democratizes access to powerful AI tools,
making them available to a wider range of users
and applications.
And their work on multilingual and long context
capabilities is pushing the boundaries of what's
possible with language models.
Absolutely.
We're getting closer to AI that can truly understand
and communicate across linguistic and cultural barriers
and process information on a scale that
was previously unimaginable.
It's like we're breaking down the communication barriers
between humans and machines, opening up
a whole new world of possibilities.
I love that.
And let's not forget their emphasis on human alignment,
ensuring that these powerful models are used responsibly
and ethically.
The Quinten team has shown that it's
possible to create models that are both highly capable
and aligned with human values.
I agree.
That's essential for building a future where AI benefits
all of humanity.
I agree.
Their research reminds us that AI development is not just
about creating smarter machines.
Right.
It's about creating a better world
with the help of those machines.
Yeah.
It's about using this incredible technology
to solve real world problems and enhance human capabilities.
Well said.
The Quinten 2.5 research is a shining
example of how technical innovation
and a deep understanding of the societal impact of AI
can go hand in hand.
OK, before we move on to the final part of our deep dive,
a question for you.
What's the most exciting aspect of this research?
What has you buzzing about the future of LLMs?
That's a tough one.
But if I had to choose, I'd say it's the work they've
done with MOMI models.
Those MOMI models, always stealing the show.
What specifically excites you about them?
I believe they have the potential
to unlock the next level of LLM capabilities.
OK.
They offer a way to scale performance without the cost.
Yeah.
Making these models more accessible and applicable
to a wider range of problems.
It's like they've found a way to break free
from the limitations of traditional model architectures.
It's like a paradigm shift in AI.
And their innovative techniques like fine-grained
expert segmentation and shared experts routing
show that there's still so much to explore
in this exciting new frontier.
It's a field ripe for innovation.
And I think we'll be talking about MOMI models a lot more
in the future.
I'm eager to see what they do next.
Me too.
OK, are you ready to move on to the final part
of this epic deep dive?
Let's do it.
All right.
We're back, ready to wrap up our deep dive into the Quinn 2.5
technical report.
This has been an incredible journey.
Yeah, it has.
And I feel like we've only scratched the surface of what
these models can do.
I agree.
We have covered a lot of ground.
But it's clear the Quinn team is pushing the boundaries
of what's possible with LLMs.
They've really set a new standard
for performance, efficiency, and accessibility.
They have.
I'm so curious to see what the future holds for this technology.
What are your thoughts?
Well, their commitment to open waiting
so many of these models is a huge gift to the AI community.
It is.
This kind of open access is going
to accelerate progress in all sorts of unexpected ways.
It's like they've thrown open the doors to their AI lab.
Yeah.
And invited the whole world to come in and explore.
That's a beautiful thing.
This research really shows the power of collaboration.
I agree.
By making these tools available to everyone,
we're going to see a burst of creativity and innovation.
I can't wait to see what people create with these models.
Imagine students using Quinn 2.5 to help them learn new languages.
Or researchers analyzing massive data sets to uncover
hidden patterns.
The possibilities are endless.
And I think we're just at the beginning
of this AI revolution.
I agree.
It's an exciting time to be alive.
But as we move forward with AI development,
we need to keep ethical considerations in mind.
Absolutely.
We want to ensure that these powerful tools are used for good
to benefit all of humanity.
Right.
The Quinn team's work is a great example
of how to combine technical brilliance
with a focus on responsible AI development.
Absolutely.
They're not just building smarter machines.
They're building a better world with AI.
Well said.
OK.
I think we've covered just about everything.
Any final thoughts before we sign off?
It's just huge thank you to you.
Oh, you're welcome.
For taking us on this incredible journey,
your insights have been so valuable.
Well, thank you.
It was my pleasure talking with you about all this.
Likewise.
And to our listeners, what stood out most to you
in this deep dive?
The million token context length.
The amazing MOE models.
Or was it something else that really blew your mind?
Let us know.
Yeah, we'd love to hear from you.
Keep those AI papers coming, and we'll
be back to break down all the exciting new developments.
Until next time, stay curious and keep exploring
the amazing world of AI.
See you on the next episode of AI Papers podcast daily.