NanoChat – The best ChatGPT that $100 can buy

https://github.com/karpathy/nanochat

271•huseyinkeles•3h ago

https://x.com/karpathy/status/1977755427569111362

Comments

daft_pink•3h ago

Wow, how do we sign up for the Eurekalabs course and how much does it cost?

huseyinkeles•3h ago

Karpathy says nanochat will become the capstone project of the course LLM101n being developed by Eureka Labs.

I guess it’s still a work in progress? Couldn’t find any other information elsewhere.

Schiphol•2h ago

A bit more info [here](https://github.com/karpathy/LLM101n)

karpathy•1h ago

Still under development, remaining work includes tuning nanochat (current state being solid v0.1) and finalizing the in-between projects so that students can "unlock" all complexity that hides underneath: `torch.Tensor`, `torch.dist`, `.backward()`, '.compile()`, etc. And then the more ops heavy aspects.

BrokenCogs•24m ago

What's the pricing for the course/EurekaLabs? P.s. thanks for all you're doing

karimf•2h ago

I've always thought about the best way to contribute to humanity: number of people you help x how much you help them. I think what Karpathy is doing is one of the highest leverage ways to achieve that.

Our current world is build on top of open source projects. This is possible because there are a lot of free resources to learn to code so anyone from anywhere in the world can learn and make a great piece of software.

I just hope the same will happen with the AI/LLM wave.

viccis•18m ago

I recommend his ANN/LLM from scratch videos to people a lot because not only is he a clear instructor, but his code tends to be very Pythonic and just the right balance of terse but readable (not counting the Pytorch vectorization stuff, but that's not his fault, it's just complex). So I think people benefit just from watching and imitating his code style.

croes•17m ago

I‘m afraid the technology will do more damage because many people will abuse it for fake news and misinformation.

shafyy•13m ago

If it only were so easy

bkettle•5m ago

This free tradition in software is I think one of the things that I love so much, but I don't see how it can continue with LLMs due to the extremely high training costs and the powerful hardware required for inference. It just seems like software will necessarily require paying rent to the LLM hosts to keep up. I guess it's possible that we'll figure out a way to do local inference in a way that is accessible to everyone in the way that most other modern software tools are, but the high training costs make that seem unlikely to me.

flakiness•1h ago

Eureka Labs: https://github.com/EurekaLabsAI

What a prolific person Andrej is. It's been more than amazing to follow along!

TheAceOfHearts•1h ago

Here's the announcement post [0] from Karpathy, which provides a bit of additional context.

[0] https://x.com/karpathy/status/1977755427569111362

dang•40m ago

Thanks - we'll put that in the toptext as well

swyx•1h ago

> Thank you to chief LLM whisperer Alec Radford for advice/guidance.

oh man an Alec x Andrej podcast would BREAK THE INTERNET... just saying... going from glory days of GPT1 to now building GPT3? in 4 hours

codybontecou•1h ago

Please oh please. This would be perfect.

mhitza•1h ago

Should be "that you can train for $100"

Curios to try it someday on a set of specialized documents. Though as I understand the cost of running this is whatever GPU you can rent with 80GB of VRAM. Which kind of leaves hobbyists and students out. Unless some cloud is donating gpu compute capacity.

portaouflop•1h ago

If I have let’s say 40gb RAM does it not work at all or just take twice as long to train?

typpilol•54m ago

Won't work at all. Or if it does it'll be so slow since it'll have to go to the disk for every single calculation so it won't ever finish.

Onavo•1h ago

A GPU with 80GB VRAM costs around $1-3 USD an hour on commodity clouds (i.e. the non-Big 3 bare metal providers e.g. https://getdeploying.com/reference/cloud-gpu/nvidia-h100). I think it's accessible to most middle class users in first world countries.

antinomicus•7m ago

Isn’t the whole point to run your model locally?

Havoc•47m ago

>If your GPU(s) have less than 80GB, you'll have to tune some of the hyperparameters or you will OOM / run out of VRAM. Look for --device_batch_size in the scripts and reduce it until things fit. E.g. from 32 (default) to 16, 8, 4, 2, or even 1.

That sounds like it could run on a 24gb GPU. Batch size of 8 would imply 20gb mem, no?

...presumably just takes forever

faxmeyourcode•38m ago

This weekend I just cracked into nanoGPT (https://github.com/karpathy/nanoGPT), an older but fabulous learning exercise where you build and train a crappy shakespeare GPT with ~0.8M parameters on a cpu. Results are about what you'd expect from that, they suck, but you can start to feel the magic, especially if you're not a deep learning professional and you just want to poke around and hack on it.

I started writing up a blog post on my weekend with nanoGPT but it's not done yet... Would have been great to link to here lol oh well

andrewljohnson•32m ago

the shakespeare code tuned a little with different training data does a good job of generating Magic The Gathering commander decks

dmarcos•24m ago

I like the idea of specific-purpose toy models. How did you tune the code and what dataset you used?

ACCount37•24m ago

It's a useful exercise. A lot of the good ML work is first validated at small scale.

And this new example goes even further - adds instruction following and tool use SFT, as well as RLVR. Makes for a more useful baseline.

CountGeek•15m ago

So could I in practice train it on all my psychology books, materials, reports, case study and research papers and then run it on demand on a 1xH100 node - https://getdeploying.com/reference/cloud-gpu/nvidia-h100 whenever I have a specialised question?

zipy124•8m ago

You could but it would be significantly worse than fine-tuning or RAG with a pre-trained model, or using a smaller model since your dataset would be so small.

cyanydeez•5m ago

if the AI bubble is anything to be compared to, how is 100$ worth anything in GPT terms.

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NanoChat – The best ChatGPT that $100 can buy

Comments

NanoChat – The best ChatGPT that $100 can buy

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