This is like a modern form of "I could do that in a weekend". Try reading the article before making such statements.
There's a lot of pre-processing, experimentation and validation that went into this project. The training data collection and sanitization alone is a big undertaking.
As for the blog post itself, from the article:
> Note: This blog post is 100% written by me. No AI has been used whatsoever.
Put another way: You can ask the LLM yourself to do this project? Please do, share your prompt, I'd like to see it.
I get what you are saying, but at the same time I was bored on a Saturday and 'vibe coded' a small VR game, nothing special, but I had the LLM throw down a structure, and then I walked through it looking at and thinking about why placement of code was how it was and how different things were handled. It was basically exactly like my job, jump into some okay working legacy app, code I have never actually seen, try to get my brain around it, then personally tweak things until the app performs the way I fully want.
That's a fair point TBH. I said in my post that this LLM is first of all a learning project and I skipped an important step: the training loop. But on the other hand, how many data scientists are writing their own training loops? Is it even worth it? And how much learning do you want for one project, I mean, where do you stop? Why use "Huggingface Transformers" when you can write it from scratch, for learning? Why use Torch when you can write it from scratch, for learning? Why use Python when you can write in C, etc. It's cheating, right?
In my case, I decided to skip the training loop and focus on the data processing and the hyper params and the rest of the higher level steps that took a ton of time anyway, and I reduced the friction.
I do get your point tho. Now that I know how to train an LLM, maybe I'll write a training loop from scratch as a project, to learn how to do it.
> These samples have very good scores overall, but they are useless. I am guessing it's not English text... I counted a few hundred examples mostly from LOC-PD and other few hundred in the OTA datasets. Imagine if I feed that crap to my LLM, what will it learn?
im pretty sure its a real text in Welsh. there might be typos from ocr but yeah thats what the language really looks like, i dont speak it but its easy to recognize.
Recently, I started a personal project to build an LLM from zero.
I've spent a ton of time reading up on math, ML, and DL through books, open courses, and papers, while also studying all the major open-source LLM architectures.
Since I only have one DGX Spark machine to run experiments, I can't train a massive LLM from the get-go. Instead, I'm experimenting with an auto-scaling parameter mechanism, which has led me to create a pretty unconventional and fun architecture!
Why go through all this effort when modern LLMs can basically write simple LLMs themselves, and I clearly can't out-compute the big tech giants?
Honestly, it's because I'm obsessed with the core mechanics of LLMs. I want to build something exclusively for myself and hopefully discover some completely undiscovered mechanisms along the way.
Just keeping a record and sharing my progress—having fun with it is truly the biggest reward!
It's mainly just my personal preference to run a local machine. It gives me better privacy and security, and I can keep all my heavy data and projects right there.
Cloud rentals are usually billed hourly. Since I constantly tweak the architecture and run it again, having a local rig completely kills any cost anxiety—it's just a one-off payment.
Plus, regular users can't even get access to H100s anyway. I applied on AWS and GCP before and couldn't get them.
I am creating my tiny Llama 340M base model from scratch. If you're curious about the steps, challenges and cost, read on. I am still working on the instruct model.
I feel like this is the true frontier, making smaller models that can do more than their predecessors. If we can crack this space to where you can get reasonable outputs from "mediocre hardware" it would be worthwhile, even if its somewhat inferior to frontier models, we can't forget that not long ago, frontier models are nowhere near as good as they are today, and tomorrow's models will likely be even better.
That's exactly what I had in mind. When I started this, I was jumping back and forth between this thought: "Can this model size actually generate logical English text?" and I played with a few different models of the same size and I was really really depressed when seeing how bad they are.... but then I discovered more and more tiny models and LaMini-125M, LaMini-256M, and nanowhale-100m, and SmolLM2-135M-Instruct are very very decent. So I decided to give it a try.
I've been working on something like this too, for quite a while! Though I'm trying to get a non-quadratic-attention LLM (or SLM) up and running.
And anyway, I think the most important thing is dataset quality. Dumping in whatever dataset you find on Huggingface is a recipe for mediocrity, so I'm also spending a lot of time on that.
Qwen seems to be going in a good direction -- hundreds of experts on their MoE models. Extremely low active-weight counts while still performing quite admirably. I look forward to models with many, many more experts, to the point where anyone with enough random access can generate hundreds or thousands of tokens per second. Because right now, 80–120t/s is pretty slow.
There are certain things you can only truly learn by doing. I remember doing Linux From Scratch over a weekend and the depth of linux that I still understand to this day.
Thanks for the writeup. A more granular followup would be cool too.
The depth of running configure, make, make install? If you want depth in Linux I recommend looking at its source repository and reading the documentation or code. Or in the current times asking AI to help explain it to you.
Instead of always trying to make models more current and general, there may be value in making them deliberately narrow, historically constrained and weird in a well-defined way
I appreciate the honesty, but now there's no journey, and that's what I'm interested in. I can ask a LLM myself.
There's a lot of pre-processing, experimentation and validation that went into this project. The training data collection and sanitization alone is a big undertaking.
As for the blog post itself, from the article:
> Note: This blog post is 100% written by me. No AI has been used whatsoever.
Put another way: You can ask the LLM yourself to do this project? Please do, share your prompt, I'd like to see it.
im pretty sure its a real text in Welsh. there might be typos from ocr but yeah thats what the language really looks like, i dont speak it but its easy to recognize.
"It will be easy for the knowledgeable to fix the few errors that remain [in the text]". (Bydd yn rwydd iawn i'r cyfarwydd ddiwygio'r ychydig.")
Which is exactly what the OP is doing.
I've spent a ton of time reading up on math, ML, and DL through books, open courses, and papers, while also studying all the major open-source LLM architectures.
Since I only have one DGX Spark machine to run experiments, I can't train a massive LLM from the get-go. Instead, I'm experimenting with an auto-scaling parameter mechanism, which has led me to create a pretty unconventional and fun architecture!
Why go through all this effort when modern LLMs can basically write simple LLMs themselves, and I clearly can't out-compute the big tech giants?
Honestly, it's because I'm obsessed with the core mechanics of LLMs. I want to build something exclusively for myself and hopefully discover some completely undiscovered mechanisms along the way.
Just keeping a record and sharing my progress—having fun with it is truly the biggest reward!
I'll share it when I get a chance!
Cloud rentals are usually billed hourly. Since I constantly tweak the architecture and run it again, having a local rig completely kills any cost anxiety—it's just a one-off payment.
Plus, regular users can't even get access to H100s anyway. I applied on AWS and GCP before and couldn't get them.
And anyway, I think the most important thing is dataset quality. Dumping in whatever dataset you find on Huggingface is a recipe for mediocrity, so I'm also spending a lot of time on that.
Thanks for the writeup. A more granular followup would be cool too.
Do you mind expanding this question? More granular in what way? what would you like to know that is missing from the post?