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.
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.
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.
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 writing 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.
I also worry that as we rely on LLMs more and more, we will stop producing the kind of tutorials and other content aimed at beginners that makes it so easy to pick up programming the manual way.
Software is just a tool. Much like a hammer, a knife, or ammonium nitrate, it can be used for both good or bad.
I say this as someone who has spent almost 15 years writing software in my free time and publishing it as open source: building software and allowing anyone to use it does not automatically make other people's lives better.
A lot of my work has been used for bad purposes or what some people would consider bad purposes - cheating on tests, cheating in games, accessing personal information without permission, and in one case my work contributed to someone's doxxing. That's because as soon as you publish it, you lose control over it.
But at least with open source software, every person can use it to the same extent so if the majority of people are good, the result is likely to be more positive than negative.
With what is called AI today, only the largest corporations can afford to train the models which means they are controlled by people who have entirely different incentives from the general working population and many of whom have quite obvious antisocial personality traits.
At least 2 billion people live in dictatorships. AI has the potential to become a tool of mass surveillance and total oppression from which those countries will never recover because just like the models can detect a woman is pregnant before she knows it, it will detect a dissenter long before dissent turns into resistance.
I don't have high hopes for AI to be a force for good and teaching people how toy models work, as fun as it is, is not gonna change it.
While documenting a build path is nice, IMHO renting hardware nobody can afford from VC-backed cloud providers using cold hard cash to produce clones of legacy tech using toy datasets under the guise of education is propping up the AI bubble and primarily helping institutional shareholders in those AI bubble companies, particularly their hardware supplier NVidia. Personally I do not see this as helping people or humanity.
This would sit better with me if the repo included a first tier use case for local execution, non-NVidia hardware reference, etc.
number of people you help x how much you help them x number of people you harm x how much you harm them
For example - harming a little bit all content creators of the world, by stealing their work without compensation or permission. How much does that cost globally every year after year? How do we even quantify long term consequences of that? Stuff like that.
(This is a bit ranty, but due to a sincere desire for a better world, and being the recipient of personal attacks for believing a better world is achievable by a different path to others)
I feel like this point of view is an ideal not shared by one of the main branches of anti-AI sentiment.
The idea of intellectual property works against this. Rather than contributing to humanity directly, ownership of information is accumulated by individuals and then rented to humanity.
At the same time I agree that people should be able to have a livelihood that affords them the ability to create new intellectual contributions.
The service Karpathy is providing is also being provided by thousands of YouTube creators in a huge variety of topics. It's a little sad that so many must support their efforts with support their efforts with sponsorships from sources with varying degrees of ethical behaviour. Patreon is better but still not ideal. I sincerely believe this _is_ one of the best ways to contribute to society.
A recent Daily Show had Jon Stewart describe training AI as strip mining human knowledge. Training AI is regularly described as theft as if this position is a given without any counter argument possible. It is opinion masquerading as fact. This saddens me because it suggests to me that the war to control the narrative is being won by people who want to entrench a hypercapitalistic vision of ownership where not only is a particular expression of an idea ownable but also stakes a claim to own some of any ideas that come from viewing that expression.
I cannot see any way that this viewpoint would aid humanity as a whole, but instead assign benefits to a collection of individuals. The ability to trade intellectual property means that ownership inevitably gets passed to a smaller and smaller pool of individuals over time.
I think we really do need a new way to consider these issues in light of the modern world. When mentioning these thoughts to others a common refrain is that it doesn't matter because the powers that be (and their lobbyists) will prevent any fix from happening. I have never been fond of that particular fatalism, especially when it inhibits discussion of what would be better.
He is the GOAT of LLM MVPs. That is educational and useful, especially because he uses a minimal and clean style, but I don't see how it even compares with kernels, operating systems etc.
So I appreciate his work in an academic and educational sense, but large scale applications with stolen training material are still theft.
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.
>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?
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
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?
You could! But just like others have mentioned, the performance would be negligible. If you really wanted to see more of a performance boost by pretraining you could try to create a bigger chunk of data to train off of. This would be done by either creating synthetic data off of your material, or finding adjacent information to your material. Here's a good paper about it:
<https://arxiv.org/abs/2409.07431>
Nice! His Shakespeare generator was one of the first projects I tried after ollama. The goal was to understand what LLMs were about.
I have been on an LLM binge this last week or so trying to build a from-scratch training and inference system with two back ends:
- CPU (backed by JAX)
- GPU (backed by wgpu-py). This is critical for me as I am unwilling to deal with the nonsense that is rocm/pytorch. Vulkan works for me. That is what I use with llama-cpp.
I got both back ends working last week, but the GPU back end was buggy. So the week has been about fixing bugs, refactoring the WGSL code, making things more efficient.
I am using LLMs extensively in this process and they have been a revelation. Use a nice refactoring prompt and they are able to fix things one by one resulting in something fully functional and type-checked by astral ty.
If you’re not writing/modifying the model itself but only training, fine tuning, and inferencing, ONNX now supports these with basically any backend execution provider without needing to get into dependency version hell.
What are your thoughts on using JAX? I've used TensorFlow and Pytorch and I feel like I'm missing out by not having experience with JAX. But at the same time, I'm not sure what the advantages are.
I only used it to build the CPU back end. It was a fair bit faster than the previous numpy back end. One good thing about JAX (unlike numpy) is that it also gives you access to a GPU back end if you have the appropriate stuff installed.
This is absolutely fantastic. I really can't wait for the final course to be live. It's in the "shut up and take my money" category. I had so much fun with the nanoGPT videos.
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[ 3.6 ms ] story [ 78.0 ms ] threadOur 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.
I also worry that as we rely on LLMs more and more, we will stop producing the kind of tutorials and other content aimed at beginners that makes it so easy to pick up programming the manual way.
Software is just a tool. Much like a hammer, a knife, or ammonium nitrate, it can be used for both good or bad.
I say this as someone who has spent almost 15 years writing software in my free time and publishing it as open source: building software and allowing anyone to use it does not automatically make other people's lives better.
A lot of my work has been used for bad purposes or what some people would consider bad purposes - cheating on tests, cheating in games, accessing personal information without permission, and in one case my work contributed to someone's doxxing. That's because as soon as you publish it, you lose control over it.
But at least with open source software, every person can use it to the same extent so if the majority of people are good, the result is likely to be more positive than negative.
With what is called AI today, only the largest corporations can afford to train the models which means they are controlled by people who have entirely different incentives from the general working population and many of whom have quite obvious antisocial personality traits.
At least 2 billion people live in dictatorships. AI has the potential to become a tool of mass surveillance and total oppression from which those countries will never recover because just like the models can detect a woman is pregnant before she knows it, it will detect a dissenter long before dissent turns into resistance.
I don't have high hopes for AI to be a force for good and teaching people how toy models work, as fun as it is, is not gonna change it.
This would sit better with me if the repo included a first tier use case for local execution, non-NVidia hardware reference, etc.
number of people you help x how much you help them x number of people you harm x how much you harm them
For example - harming a little bit all content creators of the world, by stealing their work without compensation or permission. How much does that cost globally every year after year? How do we even quantify long term consequences of that? Stuff like that.
I feel like this point of view is an ideal not shared by one of the main branches of anti-AI sentiment.
The idea of intellectual property works against this. Rather than contributing to humanity directly, ownership of information is accumulated by individuals and then rented to humanity.
At the same time I agree that people should be able to have a livelihood that affords them the ability to create new intellectual contributions.
The service Karpathy is providing is also being provided by thousands of YouTube creators in a huge variety of topics. It's a little sad that so many must support their efforts with support their efforts with sponsorships from sources with varying degrees of ethical behaviour. Patreon is better but still not ideal. I sincerely believe this _is_ one of the best ways to contribute to society.
A recent Daily Show had Jon Stewart describe training AI as strip mining human knowledge. Training AI is regularly described as theft as if this position is a given without any counter argument possible. It is opinion masquerading as fact. This saddens me because it suggests to me that the war to control the narrative is being won by people who want to entrench a hypercapitalistic vision of ownership where not only is a particular expression of an idea ownable but also stakes a claim to own some of any ideas that come from viewing that expression.
I cannot see any way that this viewpoint would aid humanity as a whole, but instead assign benefits to a collection of individuals. The ability to trade intellectual property means that ownership inevitably gets passed to a smaller and smaller pool of individuals over time.
I think we really do need a new way to consider these issues in light of the modern world. When mentioning these thoughts to others a common refrain is that it doesn't matter because the powers that be (and their lobbyists) will prevent any fix from happening. I have never been fond of that particular fatalism, especially when it inhibits discussion of what would be better.
So I appreciate his work in an academic and educational sense, but large scale applications with stolen training material are still theft.
What a prolific person Andrej is. It's been more than amazing to follow along!
[0] https://x.com/karpathy/status/1977755427569111362
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
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.
That sounds like it could run on a 24gb GPU. Batch size of 8 would imply 20gb mem, no?
...presumably just takes forever
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
>> Why is the sky blue? > The sky is blue due to an optical illusion called the Rayleigh Scattering
Rayleigh Scattering is not an illusion but an effect.
> […] particles are made up of tiny blue and violet particles that cause the light to bend in a particular way.
ugh. no, there are no "tiny blue" particles in the sky.
Will share the resulting model once ready (4 hours from now) for anyone to test inference.
I have been on an LLM binge this last week or so trying to build a from-scratch training and inference system with two back ends:
- CPU (backed by JAX)
- GPU (backed by wgpu-py). This is critical for me as I am unwilling to deal with the nonsense that is rocm/pytorch. Vulkan works for me. That is what I use with llama-cpp.
I got both back ends working last week, but the GPU back end was buggy. So the week has been about fixing bugs, refactoring the WGSL code, making things more efficient.
I am using LLMs extensively in this process and they have been a revelation. Use a nice refactoring prompt and they are able to fix things one by one resulting in something fully functional and type-checked by astral ty.