Episode 1- Efficient LLM training with Unsloth.ai Co-Founder

Summary

In this episode of 'Unsupervised Learning,' Renee interviews Daniel, co-founder of UNS, creators of the AI training system Sloth. They discuss how Sloth achieves 30 times faster fine-tuning for large language models, reducing memory usage by 50%. Daniel shares his background at Nvidia, where he optimized GPU algorithms, and how that experience influenced the development of Sloth. The conversation delves into the challenges and potential of language models, the importance of treating them as multiple agents, and the future vision of personal AI chatbots. Daniel also talks about the open-source community's role in UNS's growth and how they can support the project through collaborations and donations.

Takeaways

• 🚀 UNS (Unsupervised Learning) Sloth is an AI training system that accelerates fine-tuning of large language models by 30 times.
• 🌟 The open-source package from UNS has 3,000 GitHub stars and reduces memory usage by 50%, making fine-tuning twice as fast.
• 💡 Daniel's experience at Nvidia involved making algorithms run 2,000 times faster on GPUs, which influenced the development of UNS Sloth.
• 🏆 UNS Sloth was developed in response to the LLM Efficiency Challenge, focusing on training faster to achieve high accuracy.
• 🔍 The system rewrites the backpropagation algorithm in hard math and optimizes it for GPU code, leading to efficiency gains.
• 🌐 UNS Sloth supports training on various languages, not just English, and simplifies the conversion of models to different formats.
• 🤖 The team behind UNS Sloth uses AI, like Chat GPT, for engineering tasks and to overcome coding challenges.
• 📊 Language models have limitations in math due to tokenization issues, specialized training data, and their design not being focused on mathematical tasks.
• 📚 RAG (Retrieval-Augmented Generation) is a method for knowledge injection into language models, allowing them to search large databases for accurate answers.
• 🌐 Daniel's preferred sources for staying updated on AI include Twitter, Reddit, and YouTube channels like Yan's.
• 💸 UNS Sloth is currently bootstrapped, and they welcome community support through collaborations and donations via their GitHub page.

Q & A

What is UNS and how did it come to be?

UNS (unsupervised learning) is an open-source package developed to make the fine-tuning of language models 30 times faster. It was created by Daniel, the co-founder, with the goal of reducing the time and memory usage required for fine-tuning, making it more accessible and efficient.

How does UNS reduce memory usage during fine-tuning?

UNS reduces memory usage by 50% by optimizing the kernels in Triton language and rewriting the entire backpropagation algorithm in hard maths, which allows for more efficient memory management and faster processing.

What was Daniel's role at Nvidia and how did it influence UNS Sloth?

Daniel worked at Nvidia with the goal of making algorithms on the GPU faster. His experience in writing Cuda kernels and optimizing algorithms was applied to UNS Sloth, where he rewrote the kernels in Triton language and performed extensive code optimizations.

What is the significance of the Triton language in UNS Sloth?

Triton language is used in UNS Sloth for its performance and efficiency benefits. It allows for the creation of optimized kernels that improve the speed and memory usage of the fine-tuning process.

How does UNS Sloth address the language limitations of models like MESTRO and LLaMA?

UNS Sloth allows users to fine-tune open-source models like MESTRO and LLaMA in any language, not just English. This overcomes the limitation of these models being English-based and enables training in languages like Portuguese or Mandarin.

What are some of the challenges with language models in performing mathematical operations?

Language models often struggle with mathematical operations like multiplication and addition due to tokenization issues and the specialized nature of math problems. They may not have seen complex formulas in their training set, which limits their ability to perform certain mathematical tasks.

How does Daniel envision the future of UNS Sloth?

Daniel's vision for the future includes a fine-tuning bot on every computer, even those with weak GPUs. This bot would read personal data, perform fine-tuning daily, and learn about the user to become a personal chatbot, making AI more accessible and personalized.

What is RAG and how does it enhance language models?

RAG (Retrieval-Augmented Generation) is a knowledge injection system that allows language models to search large databases, like Wikipedia or the internet, to find correct answers. This enhances the model's ability to provide accurate and up-to-date information.

How does Daniel stay updated with the latest developments in AI?

Daniel uses Twitter for new releases, Reddit for the latest information, and YouTube for educational content. He particularly recommends Yan's YouTube videos for staying up-to-date with AI developments.

How can people support UNS and contribute to its development?

Support for UNS can come in the form of contributions to their open-source package, feature requests, or financial donations through platforms like Ko-fi (pronounced 'ko-fi' or 'coffee'), which helps fund further development and implementation of new features.

Outlines

00:00

🚀 Introduction to UNS Sloth and AI Training

Renee interviews Daniel, co-founder of UNS, about their AI training system, UNS Sloth, which accelerates fine-tuning of large language models by 30 times. They discuss the open-source package that makes fine-tuning twice as fast and reduces memory usage by 50%. Daniel shares his background at Nvidia and the inspiration behind UNS Sloth, which emerged from a competition focused on training large language models efficiently. The conversation delves into the technical aspects of optimizing GPU algorithms and the use of Triton language for kernel development.

05:00

🤖 Utilizing Chat GPT and Open Source

Daniel and Renee discuss the practical applications of UNS Sloth, including the ability to fine-tune models in different languages and the challenges faced by language models in mathematics. They touch on the use of Chat GPT for engineering tasks and the limitations of language models in handling mathematical problems due to tokenization and training data constraints. Daniel also shares his thoughts on the potential of language models to achieve AGI and the importance of treating them as a system of multiple agents rather than a single entity.

10:01

🌐 Democratizing AI and the Future of UNS Sloth

The discussion shifts to the broader goals of UNS Sloth, which include making AI accessible to everyone and reducing energy consumption. Daniel talks about the partnership with Hugging Face and the aim to enable fine-tuning on even small graphics cards. He envisions a future where everyone has a personal AI chatbot on their computer, capable of learning about the user and providing personalized assistance. The conversation also covers the misconceptions about working with large language models and the importance of using them in tandem for better results.

15:04

🔍 Keeping Up with AI and Supporting UNS

Daniel shares his sources for staying updated in the AI field, including Twitter, Reddit, and YouTube, and emphasizes the value of local communities for the latest information. He also discusses the current bootstrapped state of UNS and how people can support their work through donations and collaboration on their open-source GitHub repository. The episode concludes with a mention of the sponsor, Jan, which allows users to run any LLM locally on their devices.

Keywords

💡Unsupervised Learning

Unsupervised Learning is a type of machine learning where the model is not given any labeled data. Instead, it learns to identify patterns and structures from the input data on its own. In the context of the video, it refers to the podcast where the interview with Daniel takes place, focusing on AI and open source technology.

💡AI Training System

An AI Training System is a set of tools and processes designed to train artificial intelligence models. These systems often involve algorithms that enable the AI to learn from data. In the video, Daniel discusses UNS Sloth, an AI training system that accelerates the fine-tuning process for large language models.

💡Fine-tuning

Fine-tuning in machine learning refers to the process of adjusting a pre-trained model to a specific task or dataset. This typically involves training the model on a smaller, more specialized dataset to improve its performance. The video highlights UNS's ability to make fine-tuning 30 times faster.

💡Large Language Models

Large Language Models (LLMs) are complex AI models designed to process and generate human-like text. They are trained on vast amounts of text data and can perform various language tasks. The video discusses the challenges and innovations in fine-tuning these models more efficiently.

💡GPU

A Graphics Processing Unit (GPU) is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. In the video, GPUs are discussed as essential hardware for running AI models and the improvements made to optimize their performance.

💡CUDA

CUDA (Compute Unified Device Architecture) is a parallel computing platform and API model created by NVIDIA. It allows developers to use NVIDIA GPUs for general purpose processing. Daniel mentions his work at NVIDIA, where he made algorithms run faster on GPUs, which is relevant to the efficiency of UNS Sloth.

💡Open Source

Open source refers to a software whose source code is made available for modification or enhancement by anyone. The video emphasizes the open source nature of UNS, allowing for community contributions and improvements.

💡Triton Language

Triton Language is a programming language designed for high-performance computing, particularly for NVIDIA GPUs. It is used in the UNS Sloth system to rewrite kernels, which are essential for the system's performance optimizations.

💡Memory Usage

Memory usage refers to the amount of computer memory occupied by a program or process. In the context of the video, reducing memory usage is a key goal of UNS Sloth, as it allows for more efficient fine-tuning of large language models.

💡LLM Efficiency Challenge

The LLM Efficiency Challenge is a competition mentioned in the video where participants aim to train a large language model to achieve the highest accuracy within a limited time frame, such as one day on one GPU. This challenge inspired the development of UNS Sloth.

💡Knowledge Injection

Knowledge Injection is a technique where external knowledge, such as from a database or library, is integrated into an AI model to enhance its performance. In the video, RAG (Retrieval-Augmented Generation) is discussed as a form of knowledge injection, allowing language models to search and utilize large databases to provide accurate answers.

Highlights

Daniel, co-founder of UNS, discusses their AI training system, Sloth, which accelerates fine-tuning of large language models by 30 times.

UNS has an open-source package that makes fine-tuning two times faster and reduces memory usage by 50%.

Daniel's previous work at Nvidia focused on making algorithms run faster on GPUs, including speeding up TS and randomized SVD.

UNS Sloth was developed during the LLM Efficiency Challenge, aiming to train language models faster to achieve high accuracy.

UNS rewrote kernels in Triton language and performed extensive optimizations to improve efficiency.

UNS is agnostic to the type of GPU, supporting both Nvidia and AMD, and can fine-tune models faster than traditional methods.

The open-source package includes around 50 tricks to enhance speed and efficiency.

UNS allows fine-tuning of language models in various languages, not just English, addressing a limitation of previous models.

The community is using UNS for model conversion, leveraging the fine-tuning notebook for non-technical users.

Daniel's brother handles the non-technical aspects of UNS, including the frontend and website, while Daniel focuses on algorithms.

UNS uses Chat GPT for engineering help and to overcome roadblocks in development.

Language models struggle with math due to tokenization issues and specialized training data.

UNS aims to democratize AI by making it accessible to everyone, regardless of the size of their GPUs.

Daniel explains the misconception of treating language models as a single system, advocating for a layered approach with multiple models.

UNS has partnered with Hugging Face to make large language models more accessible.

The future vision for UNS includes a personal fine-tuning bot on every computer, providing a personal chatbot experience.

RAG (Retrieval-Augmented Generation) is a method for knowledge injection into language models, allowing them to search large databases for answers.

Daniel would like to interview someone from OpenAI who believes that language models are sufficient for AGI (Artificial General Intelligence).

Daniel uses Twitter, Reddit, and YouTube for staying up-to-date with the latest AI developments.

UNS is currently bootstrapped and open to community support through collaborations and donations.

Transcripts

[Music]

hi Renee here at unsupervised learning

your easy listening podcast for bleeding

edge open source Tech today we're

speaking to Daniel co-founder of UNS

sloth the AI training system boasting 30

times faster fine tuning for large

language models we speak on working with

family his Beginnings at Nvidia what the

hell is rag anyway and his favorite

places to keep up to date join me for

this catchup on unsupervised learning

are you able to give a quick rundown of

unsl how it came to be so we make fine

tuning of language models 30 times

faster we have an open source package

with 3,000 GitHub Stars it makes fine

tuning two times faster and it reduces

memory usage by 50% so the goal of unso

was before it can take very slow to fine

tune it can use lots of memory and now

with our like free collab notebooks you

can like just click you know run all you

can put your data set whatever you like

and then it just fine tunes and finally

at the end you can like export it to

like ggf you can export it to like VM

that's good to know so you PR Rec worked

with Nvidia is that where the uh writing

Cuda Triton kernel kind of idea came

from

Bron yeah so I used to work at Nvidia so

my goal was to make algorithms on the

GPU faster so I made like TS 2000 times

faster I made like randomized SVD faster

as well that was my like old role and so

I think I think the goal of UNS sloth

was I think it came about like last like

October there was a competition called

the llm efficiency challenge by neurs um

the goal was you have one day on one GPU

to train one llm to you know to attain

the highest accuracy so we talk a

different approach and we thought like

you know to get the highest accuracy you

can also train faster to reach that you

know that accuracy faster and so like we

diverged and we focused on you know

training faster and so like I think yeah

we did take out Nvidia experience of

writing Cuda kernels to unso and so we

rewrote you know all of the kernels in

Triton language in open a eyes Tron

language we we also did like mass

manipulations and we did lots of

optimizations in the code yeah so it's

like something I was wondering if it was

a case of those things that you don't

think will be relevant or you don't

realize will be relevant then becoming

relevant later on yeah that yeah I think

that's a fair point I think like openi

Tron language wasn't that popular but

then I think like now it's getting very

very popular because like you know you

want performance you want efficiency you

want memory usage so like Triton kernels

is a part of our system but I think like

that's only half of the system I think

the rest the other half is like all math

equations and like you know how do we

write the math equations more

efficiently yeah and your agnostic so

you run is it you run Nvidia AMD gpus

and what's the other Intel for the non

technical people like me and some other

people listening the kind of value prop

I guess is you can fine tune it way

faster than traditional methods so is it

in the meth method that is

different so what we do is we take like

the entire like back propagation

algorithm in the fine shooting process

right like the gradient descent process

and we rewrite it um in like hard maths

right so like we write all the math

equations down and then we optimize

every single line with like the correct

like the more optimal format for like

the math equations for example you can

like rack it correctly and then that can

increase speed and we can we also like

reduce memory usage by writing

everything in like cud programming so we

re rewrite everything in lowlevel GPU

code um and this can reduce memory usage

and make it faster so there's like 50

something tricks in like the open source

package and that's how we make it faster

aside from knowing you from the team at

Jan I also saw you've got heaps of

people talking about you online and I

think there was a there was a thread on

like black hat world or something like

that and it was like people were so

amazed like oh it's open source now but

they were like explain it to me in

layman terms because they they were

excited about it but they didn't know

what they could do with it so what are

you seeing like I'm in your Discord but

what are you seeing the community sort

of doing with

unso yeah so I think a few days ago

people like so for example the open

source models like mestro and llama

they're only english-based so they

somehow like trained it on their own

Like Home Country language like vmin or

like you know any Portuguese or Spanish

you can train you can like use our fine

tuning notebook to train on any language

that you like um so I think that was the

fundamental like problems of mist and

llama it's just english-based you can

also train like you know Mandarin and

other languages very simply I think that

was the most like interesting thing that

I found recently I think like some other

people they didn't even know how to like

convert like a model to like ggf or like

llama C++ like formats VM like for

example to use on Jan um and so like we

also solve that conversion step as well

and so they like currently people are

just using our notebook just for

conversion so they don't even do the

finetuning step they just do the

conversion step so that's a very

interesting use case yeah yeah and so is

your brother's is he like the

non-technical one of you two would you

say like how deep is he in the technical

side of what you're doing yeah so like

he does all the front end he does all

the website he does like everything else

that I don't want to do so I just do the

algorithms and so he does everything

else he's also like he's a very good

like chat GPT user so we use we actually

use chat GPT and like being chat for

like some engineering health so that's

so he's like that side of the equation

and I just do the algorithms yeah

obviously the whole kind of idea of this

podcast is for people like you like

really clever people to explain things

to me like I'm a child so on like

autonomous agents and stuff like that

like I don't want to dispute the

definition of agent but like do you have

because you're just a tiny team right

but you clearly have a lot that you're

doing a lot that you're shipping do you

have like you make use of that in an

autonomous way or is it more like you're

kind of Consulting something like chat

GPT to then help you with

code yeah that's a very good question I

think so I think if we wanted to make

like applications and like different

things and we get like we get like stuck

somewhere I think CH p is a very good

unstuck like a you know it can make you

unstuck on something that you get stuck

on so that's very useful I think we

haven't actually used it for like a

whole like application design like you

know everything from scratch I did use

it to like try to like do like Matrix

differentials and try to do like like

differentiation via chpt it was quite

bad so I did not use I unfortunately had

to fall back to like writing it on the

like you know on paper and doing the

differentials by hand um so I did try to

use chbt for like the maass part it's

just not that good so yeah so we just we

generally use you know you know chbt gb4

for like just unstuck mechanism can

improve productivity a lot like how do

you represent math

in a word format and then is that why it

has issues like why you can't trust it

for maths so I think in terms of maths I

think there was like three problems with

like language models the first one is

the tokenization problem so I think gbd4

so from what I understand gbd4 tokenizes

numbers by like you know 0er 1 two 3

four 5 six seven eight nine they

tokenize individual numbers but they

also do like double digits so like you

know 10 to 99 they also tokenize each

one has like a ID and then like 100 to

1,000 they take like individual ID so I

know like you know language models are

very bad at like multiplication they're

quite bad at addition it's because of

the tokenization issue so I know like

llama fixed fixed it as well by

tokenizing each so for example if you

have a number 100 right it would

tokenize each like they will split the

numbers into individual digits right so

one zero zero so that's one of the

issues the other issue is I guess the

training data is just too less like you

know in maths there's like in maths each

problem is like very specialized if you

want to take the differential of like X

squ that's that's easy that's 2x right

so like but then if the formula gets

very like complicated it might have not

seen it in the training set and so I

think you know once they have a larger

training set this could you know by the

phenomenon of grocking maybe they can do

some maths so there was like a I think

that people talk about qar um so

supposedly qar like can solve you know

like high school maths level problems so

this was open a i thing so those are the

two main problem s and the third one I

guess is just like I don't think so

language models are design for maths so

you need to like maybe have like a

separate component to like verify the

maths like a proof machine or like

python to write the maths um so those

are the three main problems so thank you

for the context by the way so when you

spoke about the current kind of what

unslot is being used for your recent

kind of partnership like is that part of

something bigger or is it like part of

an open source plan so we did do a

partnership with hugging face on a Blog

so we did a Blog with them we also in

the TRL docs and so we we're trying to

like make like you know llms accessible

to everyone and so our goal is to make

you know even the very small graphics

cards I think like the biggest

difference is like you have like a 8 GP

eight gigabyte like you know vram on

your GPU people before couldn't even

train on like a very you know on a 8GB

graphics card but now we made it like

you know fit just you know just there It

just fits in AGB I and so like we can

like we essentially our goal is to make

AI like you know easily available to

everyone reduce energy usage because

this training two times faster we also

want to reduce energy usage and we just

want to make it more productive for

people so that's yeah the goal is to

democratize AI for everyone but the only

to find tuning space for now

[Music]

yeah so what's something that people get

wrong about working with llms yeah

that's a great question I think the

biggest issue would be like people treat

llms like as one system so like when you

call in an llm you might get an answer

but I think that's the wrong way to go

about it you're supposed to treat llms

as like this one agent like you know

you're supposed to use llms as if like

you have like 10 of them right so like

you build layers on top of each layer so

one llm you can tell it okay you are a

you are a verify right you verify

someone's answers and then just

someone's answers is another llm which

generates answers right so one one llm

does the generation of your answer one

of them does the verification one of

them does the implementation right and

execution of your answer right so like

you need to I think the biggest

misconception is like like you know a

language model is this very powerful

system that can answer every single

question but I think like you need to

prompt it correctly and then like you

know use many of them in tandom together

and this can be more powerful than just

using like one system I think like the

biggest issue is I can see feel like

people using it as one system and then

they like you know constantly repeat

answers and it sometimes doesn't give

the correct answer and it sometimes

forgets like what you said before so I

think that way to solve this problem is

to use many of them together CPU GPU

we're gonna go right back to the start

Daniel because CPU is

what CPU is like the processor on the

computer so like so the GPU is like a

graphics card for like gaming and like

you know uh run like language models and

you know your computer display you can

have like a CPU but then if you want to

play games it'll be so slow so you need

to add this extra Hardware called the

GPU you shove it in your computer and

that is the GPU it's like an extra it

makes like you know Graphics faster and

that's h100s is that oh that's very

expensive so h100s are like extremely

pricey yeah so nvidia's like I think you

probably have one like now in a computer

like RTX 39 RTX like 29 you you probably

do have a GPU in your like computer yeah

I can run I can run some things on Jan

I've got like an it's a Mac it's a m

something M1 yeah see what what we're

working with here uh this is the yeah

the idea is if if AI is going to be

accessible and democratized it means

that you know potato people people

running potatoes and people who have

potato brains need to understand it so

that's not a roast that's just a reality

of the fact that I think that there's so

much technology that's being being

developed that is so Advanced but we

have no way of communicating it and it

sits in this very kind of

uh closed off like you know you need to

like like it's yeah all the advancements

are like you know very hard to like

access and stuff yeah it's it's

unintentional but I think like there's

so much that happens in little tiny

subreddits that the world needs to kind

of know about and like I don't go in

there because I'm scared but I see it

and I'm like oh that that would be

interesting if I knew what that was

what's a GPU but but uh yeah so no no

like yeah look it's like Olden like you

know we generally run like CPUs there's

like two right there's a CPU and there's

GPU um technically GPU is not even

necessary it's like it's optional you

just if you want it to be faster you

know more of responsive you can buy a

GPU you can still do everything on a CPU

it's just very slow

yeah so bind tuning is the current

and the the future is fluid I think the

goal is to like our vision is in the

future like everyone will have a

graphics card on your computer it might

be very weak like you know but but I

think everyone will have a GPU on the

computer and if this is the case we want

to provide like a fine-tuning bot on

your PC it reads all of your personal

data it does not upload any data this is

your personal chatbot that you have on

your local machine and it does fine

tuning every single day and so this this

model will learn about you and you can

ask questions whoever however you like

so this become like a personal chbt a

personal chbt for you only and so that's

kind of our ultimate Mission that's yeah

but that's like a future plan yeah yeah

like a like a a non crappy

clippy but yeah exactly right yeah it's

yeah it's a personal chpt for everyone

yeah yeah and so I guess a lot of the

conversations in commercial use cases

always that they love the idea of local

because it's local that's how I came to

kind of learn about what rag is and that

was like a whole thing for me so I was

like oh so that to my understanding this

is like semi off topic but you're a

person who knows in front of me so I'm

gon to ask you that rag is a way of

verifying the information that you get

back from a large language model is that

right so yes so rag I guess is like

knowledge injection so like pretend you

ask a question's today's date right so a

language w't know that right it doesn't

understand anything about like current

events so what you can do is you can

take all of Wikipedia right shove all of

Wikipedia as like a library and allow

the language model to search in the

library for the answer right so like if

I ask the question again what is today's

date Wikipedia will say you know

somewhere on the homepage today's date

is you know whatever the date is right

and so like the langu you can use rag

which is retrial or generation to like

search a big library for the correct

answer so that's rag nice so it's

different is it different from like chat

GPT or B accessing the internet that's

just different right oh that's that's

also rag so like so assume so you know

like when I said like the Wikipedia is

like a library assume Google the

internet is the library right so like

it's just replace replace Wikipedia with

Google like Google search replace

Wikipedia with like you know with

something else like you know if you want

to do like movies if you have a

complicated question about a movie you

just replace it with like the IMDb data

like you know database if you have like

a law question replace it with like

Supreme Court like you know judgments or

something so rag is just like knowledge

injection system you just need to have a

big database yeah nice I've got two more

questions the first one is who would you

interview in the AI space if you could

interview someone in the AI space

I think I would like to talk to an

interview maybe ier from open AI I think

I've watched his videos and understood

his views I I think his main his very

interesting take is language models are

enough for AGI um because he like most

people think that if you predict the

next word right it's just language

models are just predicting the next word

right it's nothing special it's not like

intelligence or something right but then

his take is if you want to predict the

next word before it you need to

understand everything about the world

right like if you want to predict

something if you want to predict the

next word you need to understand

everything the context of everything

about it you know what what why is the

word after these few words you need to

understand everything in the world and

so like his take is very fascinating I

think I would like to yeah if I had a

chance I would talk to him

yeah did you did you go to the Sam

Oldman chat in I think it was Sydney

like last year no I did not it's funny

it was in Sydney but I didn't go yeah

was I think right yeah where do you keep

up with everything because I know that

when you work in AI you actually don't

sleep it's not allowed it's in the rules

and I said like was it an RSS feed like

to have a favorite YouTube or is it just

discords

Reddit yeah I think I I think Twitter's

generally useful for like new releases

of stuff Twitter's yeah Twitter's pretty

good R yeah local llama is very useful I

think like local Lama has like all the

latest information very very useful I

also like YouTube videos are generally

good I think they're a bit delayed

though I think YouTube like if you want

to stay at the very edge there will be

like one week delayed I like like Yan's

like YouTube videos his very useful like

his videos are very helpful I watch them

like all the time so Yan is my

recommendation yeah that's if you got

like if you want to stay up to date with

everything so Twitter Reddit and I guess

YouTube I guess this sounds like

everyone like it sounds like General

like general advice um yeah yeah and

you're currently

bootstrapped where can

people uh support you and how can people

support you I know that you have a is it

is it pronounced Kofi or coffee I

actually don't know Kofi I I call it

Kofi or is it coffee I don't know I

always thought it was coffee or probably

I don't know I'm gonna message them and

ask but you have one and that is at

unoth actually don't know what's the

coffee

link I think we just typed it's on a

GitHub so we like we have like an open

source package you know we're open to

any collaborators from like the open

source Community if you want to add like

new feature requests or pool request

more than happy if you want to support

our work we have like a coile link um so

like you can donate like some money to

us and we can like Implement more

features quicker like I think we do have

like some collab expenses but there's

not that much for like testing testing

stuff out so I think that's the main

expense we have but yeah more than happy

to like accept community help if that's

what you like yeah nice thank you very

much thanks for joining me on

unsupervised learning we were speaking

to Daniel from

unso this episode was sponsored by Jan

run any llm locally on your device the

Mac windows and Linux see the open Souls

GitHub repo at Jan

[Music]

HQ