Microgpt (karpathy.github.io)

What is the prime use case

Beautiful work

Which license is being used for this?

This could make an interesting language shootout benchmark.

If anyone knows of a way to use this code on a consumer grade laptop to train on a small corpus (in less than a week), and then demonstrate inference (hallucinations are okay), please share how.

This is like those websites that implement an entire retro console in the browser.

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Karapthy with another gem !

C++ version - https://github.com/Charbel199/microgpt.cpp?tab=readme-ov-fil...

Rust version - https://github.com/mplekh/rust-microgpt

It’s pretty staggering that a core algorithm simple enough to be expressed in 200 lines of Python can apparently be scaled up to achieve AGI.

Yes with some extra tricks and tweaks. But the core ideas are all here.

This is beautiful and highly readable but, still, I yearn for a detailed line-by-line explainer like the backbone.js source: https://backbonejs.org/docs/backbone.html

I had good fun transliterating it to Rust as a learning experience (https://github.com/stochastical/microgpt-rs). The trickiest part was working out how to represent the autograd graph data structure with Rust types. I'm finalising some small tweaks to make it run in the browser via WebAssmebly and then compile it up for my blog :) Andrej's code is really quite poetic, I love how much it packs into such a concise program

Why there is multiple comments talking about 1000 c lines, bots?

Since this post is about art, I'll embed here my favorite LLM art: the IOCCC 2024 prize winner in bot talk, from Adrian Cable (https://www.ioccc.org/2024/cable1/index.html), minus the stdlib headers:

  #define a(_)typedef _##t
  #define _(_)_##printf
  #define x f(i,
  #define N f(k,
  #define u _Pragma("omp parallel for")f(h,
  #define f(u,n)for(I u=0;u<(n);u++)
  #define g(u,s)x s%11%5)N s/6&33)k[u[i]]=(t){(C*)A,A+s*D/4},A+=1088*s;
  
  a(int8_)C;a(in)I;a(floa)F;a(struc){C*c;F*f;}t;enum{Z=32,W=64,E=2*W,D=Z*E,H=86*E,V='}\0'};C*P[V],X[H],Y[D],y[H];a(F
  _)[V];I*_=U" 炾ોİ䃃璱ᝓ၎瓓甧染ɐఛ瓁",U,s,p,f,R,z,$,B[D],open();F*A,*G[2],*T,w,b,c;a()Q[D];_t r,L,J,O[Z],l,a,K,v,k;Q
  m,e[4],d[3],n;I j(I e,F*o,I p,F*v,t*X){w=1e-5;x c=e^V?D:0)w+=r[i]*r[i]/D;x c)o[i]=r[i]/sqrt(w)*i[A+e*D];N $){x
  W)l[k]=w=fmax(fabs(o[i])/~-E,i?w:0);x W)y[i+k*W]=*o++/w;}u p)x $){I _=0,t=h*$+i;N W)_+=X->c[t*W+k]*y[i*W+k];v[h]=
  _*X->f[t]*l[i]+!!i*v[h];}x D-c)i[r]+=v[i];}I main(){A=mmap(0,8e9,1,2,f=open(M,f),0);x 2)~f?i[G]=malloc(3e9):exit(
  puts(M" not found"));x V)i[P]=(C*)A+4,A+=(I)*A;g(&m,V)g(&n,V)g(e,D)g(d,H)for(C*o;;s>=D?$=s=0:p<U||_()("%s",$[P]))if(!
  (*_?$=*++_:0)){if($<3&&p>=U)for(_()("\n\n> "),0<scanf("%[^\n]%*c",Y)?U=*B=1:exit(0),p=_(s)(o=X,"[INST] %s%s [/INST]",s?
  "":"<<SYS>>\n"S"\n<</SYS>>\n\n",Y);z=p-=z;U++[o+=z,B]=f)for(f=0;!f;z-=!f)for(f=V;--f&&f[P][z]|memcmp(f[P],o,z););p<U?
  $=B[p++]:fflush(0);x D)R=$*D+i,r[i]=m->c[R]*m->f[R/W];R=s++;N Z){f=k*D*D,$=W;x 3)j(k,L,D,i?G[~-i]+f+R*D:v,e[i]+k);N
  2)x D)b=sin(w=R/exp(i%E/14.)),c=1[w=cos(w),T=i+++(k?v:*G+f+R*D)],T[1]=b**T+c*w,*T=w**T-c*b;u Z){F*T=O[h],w=0;I A=h*E;x
  s){N E)i[k[L+A]=0,T]+=k[v+A]*k[i*D+*G+A+f]/11;w+=T[i]=exp(T[i]);}x s)N E)k[L+A]+=(T[i]/=k?1:w)*k[i*D+G[1]+A+f];}j(V,L
  ,D,J,e[3]+k);x 2)j(k+Z,L,H,i?K:a,d[i]+k);x H)a[i]*=K[i]/(exp(-a[i])+1);j(V,a,D,L,d[$=H/$,2]+k);}w=j($=W,r,V,k,n);x
  V)w=k[i]>w?k[$=i]:w;}}

Incredibly fascinating. One thing is that it seems still very conceptual. What id be curious about how good of a micro llm we can train say with 12 hours of training on macbook.

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Is there something similar for diffusion models? By the way, this is incredibly useful for learning in depth the core of LLM's.

I wrote a C++ translation of it: https://github.com/verma7/microgpt/blob/main/microgpt.cc

2x the number of lines of code (~400L), 10x the speed

The hard part was figuring out how to represent the Value class in C++ (ended up using shared_ptrs).

Hoenikker had been experimenting with melting and re-freezing ice-nine in the kitchen of his Cape Cod home.

Beautiful, perhaps like ice-nine is beautiful.

Someone has modified microgpt to build a tiny GPT that generates Korean first names, and created a web page that visualizes the entire process [1].

Users can interactively explore the microgpt pipeline end to end, from tokenization until inference.

[1] English GPT lab:

https://ko-microgpt.vercel.app/

Super useful exercise. My gut tells me that someone will soon figure out how to build micro-LLMs for specialized tasks that have real-world value, and then training LLMs won’t just be for billion dollar companies. Imagine, for example, a hyper-focused model for a specific programming framework (e.g. Laravel, Django, NextJS) trained only on open-source repositories and documentation and carefully optimized with a specialized harness for one task only: writing code for that framework (perhaps in tandem with a commodity frontier model). Could a single programmer or a small team on a household budget afford to train a model that works better/faster than OpenAI/Anthropic/DeepSeek for specialized tasks? My gut tells me this is possible; and I have a feeling that this will become mainstream, and then custom model training becomes the new “software development”.

Great stuff! I wrote an interactive blogpost that walks through the code and visualizes it: https://growingswe.com/blog/microgpt

"everything else is just efficiency" is a nice line but the efficiency is the hard part. the core of a search engine is also trivial, rank documents by relevance. google's moat was making it work at scale. same applies here.