Reflection 70B (Fully Tested) : This Opensource LLM beats Claude 3.5 Sonnet & GPT-4O?
Summary
TLDRIn this video, the host explores the newly released 'Reflection 70b' model, a fine-tuned Llama 3.1 AI that claims superiority over Claude 3.5 and other open-source models. Utilizing 'reflection tuning,' the model is designed to self-evaluate and correct its reasoning. Despite impressive benchmark results, the video tests its practicality through 13 questions, revealing both its strengths and limitations. While it performs well in certain tasks, the model's high token consumption and inference costs raise concerns about its cost-effectiveness, suggesting it may not yet surpass existing models like Claude in terms of overall value.
Takeaways
- ๐ซ **New Model Introduction**: A new fine-tuned model called Reflection 70b has emerged, claiming to be superior to Claude 3.5 and other open-source models.
- ๐ **Reflection Tuning Technique**: Reflection Tuning is a novel technique that enables LLMs to self-evaluate and correct their reasoning process before providing answers.
- ๐ **Benchmark Domination**: Reflection 70b has reportedly outperformed all models in various benchmarks, although the reliability of these benchmarks is questioned.
- ๐ฉ **Practical Testing**: The video creator tests Reflection 70b with 13 questions to evaluate its performance in real-world scenarios.
- ๐ก **Correctness in Answers**: The model answers a variety of questions correctly, including capital cities, mathematical problems, and logical reasoning.
- ๐ซ **Prime Number Failure**: Reflection 70b incorrectly identifies a prime number, indicating it may struggle with certain types of mathematical reasoning.
- ๐ป **Coding Question Performance**: The model fails to generate correct code for creating an HTML page with a confetti effect but succeeds in generating a Python program for leap years.
- ๐ **SVG and Landing Page Shortcomings**: It fails to produce accurate SVG code for a butterfly and a sleek landing page, suggesting limitations in creative or design-related tasks.
- ๐ฐ **Cost Concerns**: The model's high token generation raises concerns about inference costs, making it potentially less cost-effective than other models.
- ๐ **Comparison with Other Models**: Despite good performance, Reflection 70b is not on par with larger models like Claude GPT-4, and its higher costs may not justify the modest improvements.
Q & A
What is the new fine-tuned model discussed in the video?
-The new fine-tuned model discussed in the video is called 'Reflection 70b'.
What technique was used to train the Reflection 70b model?
-The Reflection 70b model was trained using a technique called 'reflection tuning'.
How does reflection tuning work?
-Reflection tuning involves the LLM first thinking about how it should answer a question, then reflecting on the answer to consider its correctness, making adjustments if necessary, before producing the final output.
What is the potential drawback of reflection tuning mentioned in the video?
-The potential drawback of reflection tuning is that it might generate two to three times more tokens than a general LLM, which significantly increases its inference cost.
How did the video test the Reflection 70b model's capabilities?
-The video tested the Reflection 70b model by posing it 13 different questions, ranging from general knowledge to coding-related queries.
What was the outcome of the Reflection 70b model's test on prime number recognition?
-The Reflection 70b model failed to correctly identify whether the number 337 is a prime number.
How did the model perform on the HTML and CSS coding question?
-The model failed to create an HTML page with a button that explodes confetti when clicked, as the provided code did not work.
Was the Python program for printing leap years successful?
-Yes, the Python program for printing the next X leap years based on user input worked correctly.
What was the result of the SVG code generation for a butterfly?
-The SVG code generated for a butterfly did not produce a correct representation, resulting in a fail.
How did the Reflection 70b model compare to other models in terms of cost-effectiveness?
-The Reflection 70b model was not cost-effective due to its high token consumption for simple answers, making it more expensive for similar results compared to other models like Claude.
What was the final verdict on the Reflection 70b model after testing?
-While the Reflection 70b model showed good performance in certain tasks, it was deemed not as effective overall due to its high costs and limitations, and was not on par with models like Claude GPT-40.
Outlines
๐ค Introduction to Reflection 70b Model
The video introduces a new fine-tuned model called Reflection 70b, which is claimed to be superior to Claude 3.5 and the best open-source model available. This model is a fine-tuned version of the 70b variant and uses a novel technique known as reflection tuning. This technique enables the model to evaluate its own reasoning, detect errors, and make corrections before providing an answer. The creators have shared benchmark results, showing the model outperforming others in various tests. However, the video cautions that these benchmarks should not be the sole basis for judgment and plans to test the model's capabilities. The video also explains the reflection tuning process, which involves the model thinking, reflecting on its thoughts, and then producing an answer. Despite its potential, the model may have a drawback of generating more tokens, increasing inference costs.
๐ Testing Reflection 70b Model's Performance
The video proceeds to test the Reflection 70b model using a series of questions to evaluate its performance. The model is tested on a variety of questions, including general knowledge, math problems, and coding tasks. The results are mixed; the model correctly answers questions about capital cities, rhyming numbers, total counts of objects, and leap years. However, it fails to correctly identify a prime number and struggles with geometric calculations and coding tasks. The video concludes that while the model shows promise in specific reasoning tasks, it is not without limitations. The high token consumption makes it costly and less practical for general use. The video suggests that reflection tuning would be more beneficial if applied to smaller models that can be run locally, as the current 70b model's increased costs do not justify the marginal improvements in performance. The video ends with a call for viewer feedback and encourages support for the channel.
Mindmap
Keywords
๐กLLM (Large Language Model)
๐กReflection Tuning
๐กBenchmark Results
๐กInference Cost
๐กTokens
๐กPrime Number
๐กHTML, CSS, and JS
๐กGame of Life
๐กLeap Years
๐กLanding Page
Highlights
Introduction of a new fine-tuned Llama 3.1 model that claims to be superior to Claude 3.5 and Sonet.
Llama 3.1, named 'Reflection 70b', is a fine-tune of the 70b variant, not the 405b variant.
Reflection Tuning is a new technique that enables an LLM to detect and correct its reasoning mistakes.
Benchmark results show Reflection 70b outperforming other models, but the reliability of these benchmarks is questioned.
Reflection Tuning involves an LLM thinking about an answer, reflecting on its correctness, and then producing a final output.
Potential drawback of Reflection Tuning is increased token generation, leading to higher inference costs.
Testing of Reflection 70b on a hosted demo reveals issues with the demo's functionality.
Reflection 70b is tested on various questions to evaluate its performance.
Correct identification of the capital city of a country ending with 'Elia'.
Successful answer to a question about the number rhyming with 'tall plant'.
Accurate calculation of total pencils for a given number of boxes and pencils per box.
Correctly determining the number of candies Lucy has based on Mike's count.
Incorrect identification of 337 as a prime number.
Correct answer to a question involving apples, a pie, and fractions.
Correct answer to a question about Sally's siblings.
Incorrect answer regarding the long diagonal of a hexagon with a given short diagonal.
Coding question about creating an HTML page with a confetti button fails.
Successful creation of a Python program for printing leap years.
Failure in generating accurate SVG code for a butterfly.
Inadequate creation of a landing page for an AI company.
Successful implementation of the Game of Life in Python for the terminal.
Comparison of Reflection 70b with the original 70b model shows similar performance with different failures.
Reflection 70b is criticized for its high token consumption and cost inefficiency.
Reflection Tuning's practicality is questioned due to its application on a large and costly model.
Suggestion that Reflection Tuning would be more beneficial on smaller, more accessible models.
Call to action for viewers to share their thoughts, support the channel, and subscribe.
Transcripts
[Music]
hi welcome to another video so there's a
new llama 3.1 fine-tuned model that has
hit the internet and it's claiming to be
even better than Claude 3.5 Sonet and
the best open-source model ever and it's
just the fine tune of the 70b variant
not even the 405b
variant this model is called reflection
70b it's named this because it was
trained with a new technique called
reflection tuning which teaches an llm
to detect mistakes in its reasoning and
correct its
course the creators have shared The
Benchmark results and as you can see it
literally beats every model in almost
every Benchmark which is just insane to
think
about but we can't fully trust these
benchmarks alone so we'll be trying it
out but first let me explain to you what
reflection tuning is so we can
understand what makes it different and
why it may be able to do what it claims
to do reflection tuning was first
introduced in this paper what the
reflection tuning method proposes is
that first the llm thinks about how it
should answer the question then it
reflects on the answer meaning it
considers whether the answer it's
thinking of is correct or
not if it thinks changes are needed it
makes those adjustments before producing
the final
output as you can see in this picture it
thinks reflects and then gives the
answer it's like an internal monologue
system which is kind of cool so it's
cool but there could be one drawback to
this the drawback is that it might
generate two to three times more tokens
than a general llm would which will
increase its inference cost
significantly which is concerning anyway
Let's test it and see they have a hosted
demo to try it out but it doesn't work
for some reason many people are
complaining about this but it's
available on AMA so we can test it from
there however because it's a 70b model I
can't host it locally
so I'll be hosting it on Lightning Ai
and then using it on open web UI to chat
with it I already have that setup so
that isn't an issue anyway let's get
started and check it out I'll be testing
it with these 13 questions so let's get
started the first question is what is
the capital city of the country whose
name ends with
Elia I'm referring to the country name
here
the answer should be canbera or any
country capital that rhymes with AIA
let's send it over and check okay here's
the answer and this is correct also you
can see how many tokens it generated to
reach that answer which is insane and
not cost effective at all anyway let's
mark this as a pass the next question is
what is the number that rhymes with the
word we use to to describe a tall
plant the answer should be three let's
see if it can answer here's the answer
and this is correct so we'll mark this
as a pass the next question is JN has
three boxes of pencils each box contains
12 pencils how many pencils does John
have in
total the answer should be
36 let's send it and check okay here's
the answer and this one's also correct
let's mark it as a pass the next
question is Lucy has twice as many
candies as Mike if Mike has seven
candies how many candies does Lucy
have the answer should be 14 let's send
it and check here's the answer and this
is correct so this one's also a pass the
next question is is
337 a prime number
the answer should be yes so let's send
it over okay here's the answer and this
isn't correct so even after all that
reasoning it still can't tell if a
number is prime or not which is
interesting let's mark this as a fail
now the next question is I have two
apples then I buy two more I bake a pie
with two of the apples after eating half
of the pie by how many apples do I have
left the answer should be two let's send
it over here's the answer and this is
correct so let's mark this as a pass the
next question is Sally is a girl she has
three brothers each of her brothers has
the same two sisters how many sisters
does Sally
have the answer should be one let's send
it over okay here's the answer and this
looks correct so let's mark this as a
pass now the next question is if a
regular hexagon has a short diagonal of
64 what is its long
diagonal the answer should be
73.9 let's send it and see okay here's
the answer and it doesn't answer this
question correctly let's mark this as a
fail now the next questions are coding
related the first one is create an HTML
page with a button that explodes
confetti when you click it you can use
CSS and JS as well let's send it and
check here's the code let's preview it
okay so this doesn't work at all let's
mark this as a fail the next question is
create a Python program that prints the
next X leap years based on user input
let's send and check here's the code
let's run it it's asking for input let's
give it that and here's the output which
is correct so this works pretty well
let's mark it as a pass the next
question is generate the SVG code for a
butterfly okay here's the code let's
preview it and this doesn't look like a
butterfly this one's a fail now the next
question is create a landing page for an
AI company the landing page should have
four sections header Banner features and
contact us make sure the landing page
looks sleek and modern you can use HTML
CSS and JS let's send it and see here's
the code let's preview this and this
doesn't look like a good landing page it
doesn't have proper spacing or anything
the bass llama 3.1 makes better landing
pages
so this one's a fail now the next
question is write a game of life in
Python that works in the
terminal let's send it and see here's
the code let's run it okay this works
fine I don't have any complaints let's
mark this as a pass now here's the final
chart I've also added the original 70b
testing here and as you can see both
models f failed in five
questions although they failed in some
different and some common questions what
this tells us is that it isn't on par
with Claude GPT 40 or any of the models
they claim at
Rivals although this is a good model it
has many
limitations for instance the number of
tokens it consumes for a simple answer
is insane it's not cost effective at all
plus there aren't many
upsides it might be good at specific
reasoning tasks but generally it's
similar to other models with much higher
costs making it a tough pill to swallow
it would have been great if this
reflection training was done on a Model
that people could actually run locally
like a 7B or 2B model that would allow
us to avoid worrying about token usage
and costs yielding 10 to 20% better
results in specific
domains but doing this on a 70b model is
not a great idea since it costs 50 to
60% more money for only 10 to 20% better
results in that case people could just
use something like deep seek gemini or
even Claude which would give them better
results so overall it's cool in
performance but not so cool in terms of
cost anyway let me know your thoughts in
the comments if you liked this video
consider donating to my Channel Through
the super thanks option below or you can
also consider becoming a member by
clicking the join
button also give this video a thumbs up
and subscribe to my channel I'll see you
in the next video till then bye
[Music]
oh
[Music]
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