Reflection 70B (Fully Tested) : This Opensource LLM beats Claude 3.5 Sonnet & GPT-4O?

Summary

In 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

• 🐫 **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.
• 🔍 **Reflection Tuning Technique**: Reflection Tuning is a novel technique that enables LLMs to self-evaluate and correct their reasoning process before providing answers.
• 📊 **Benchmark Domination**: Reflection 70b has reportedly outperformed all models in various benchmarks, although the reliability of these benchmarks is questioned.
• 🚩 **Practical Testing**: The video creator tests Reflection 70b with 13 questions to evaluate its performance in real-world scenarios.
• 💡 **Correctness in Answers**: The model answers a variety of questions correctly, including capital cities, mathematical problems, and logical reasoning.
• 🚫 **Prime Number Failure**: Reflection 70b incorrectly identifies a prime number, indicating it may struggle with certain types of mathematical reasoning.
• 💻 **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.
• 📈 **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.
• 💰 **Cost Concerns**: The model's high token generation raises concerns about inference costs, making it potentially less cost-effective than other models.
• 📉 **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

00:00

🤖 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.

05:02

📊 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.

Keywords

💡LLM (Large Language Model)

A Large Language Model (LLM) refers to complex artificial neural networks that are trained on vast amounts of text data to generate human-like text. They are designed to understand and produce language with a high degree of accuracy. In the video, the script discusses a new fine-tuned LLM called 'Reflection 70b,' which is claimed to be superior to existing models like Claude 3.5. The video's theme revolves around evaluating the capabilities and performance of this new model.

💡Reflection Tuning

Reflection Tuning is a novel technique introduced in the script, which is used to train the 'Reflection 70b' model. It involves the LLM first thinking about how it should answer a question, then reflecting on the answer to determine its correctness, and making adjustments if necessary before producing the final output. This technique is central to the video's narrative as it sets the 'Reflection 70b' model apart from others by enhancing its reasoning capabilities.

💡Benchmark Results

Benchmark results are a set of standardized tests used to evaluate the performance of a model or system. In the context of the video, the script mentions that the 'Reflection 70b' model has benchmark results that show it outperforming other models in almost every test. These results are significant as they provide a comparative analysis of the model's capabilities against industry standards.

💡Inference Cost

Inference cost refers to the computational resources required to run a model and generate outputs. The video script points out a potential drawback of the 'Reflection 70b' model, which is that it might generate two to three times more tokens than a general LLM, significantly increasing its inference cost. This is a critical consideration for users as higher inference costs can make the model less practical for widespread use.

💡Tokens

In the context of LLMs, tokens refer to the basic units of text, such as words or characters, that the model processes. The script mentions that the 'Reflection 70b' model generates a high number of tokens to reach an answer, which is not cost-effective. This highlights a trade-off between the model's performance and the resources required to achieve that performance.

💡Prime Number

A prime number is a natural number greater than 1 that has no positive divisors other than 1 and itself. In the video, the script describes a test where the 'Reflection 70b' model fails to correctly identify if a number is prime, showcasing a limitation in its reasoning capabilities despite its advanced tuning technique.

💡HTML, CSS, and JS

HTML (HyperText Markup Language), CSS (Cascading Style Sheets), and JS (JavaScript) are the core technologies used for creating web pages and web applications. The video script includes tests where the 'Reflection 70b' model is asked to generate code for web-related tasks, such as creating an HTML page or a landing page. These tests evaluate the model's ability to produce functional and aesthetically pleasing web content.

💡Game of Life

The 'Game of Life' is a cellular automaton devised by the British mathematician John Horton Conway. In the video, the script describes a test where the 'Reflection 70b' model is asked to write a Python program for this game. The successful execution of the program in the terminal demonstrates the model's ability to generate complex, logic-based code.

💡Leap Years

A leap year is a year containing one extra day in addition to the usual 365. The video script includes a test where the 'Reflection 70b' model is asked to create a Python program that prints the next X leap years based on user input. The correct output of the program indicates the model's capability to handle calendar-related calculations and user interactions.

💡Landing Page

A landing page is the entry point of a website, often used for marketing or advertising campaigns. The script describes a test where the 'Reflection 70b' model is tasked with creating a landing page for an AI company. The evaluation of the landing page's design and functionality serves as a measure of the model's ability to generate user-friendly and visually appealing web content.

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]