GPU-Puzzles
picoGPT
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GPU-Puzzles | picoGPT | |
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12 | 7 | |
5,022 | 3,081 | |
- | - | |
3.4 | 1.9 | |
4 months ago | about 1 year ago | |
Jupyter Notebook | Python | |
MIT License | MIT License |
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GPU-Puzzles
- Solve Puzzles. Learn CUDA
- GPU Puzzles
- Understanding Automatic Differentiation in 30 lines of Python
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FlashAttention-2, 2x faster than FlashAttention
I found it helpful to start with CUDA on numba since it lets you write GPU kernels in python. Assuming you're like most ML engineers and you're more familiar with python than C++, this allows you to separately learn CUDA concepts from also learning C++ at the same time. There's also a set of GPU puzzles for beginners [1] using to get started with numba CUDA.
[1] https://github.com/srush/GPU-Puzzles
- [Computer Science] srush/GPU-Puzzles: Solve puzzles. Learn CUDA.
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Build on AWS Weekly - S1 E2 - Breaking Blocks with Terraform
Are you having fun with Machine Learning? Go and teach yourself beginner GPU programming with this wonderful notebook: GitHub repo
- GPU-Puzzles: Solve Puzzles. Learn CUDA
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[D] What are some good resources to learn CUDA programming?
Practice puzzles: https://github.com/srush/GPU-Puzzles
- Learn GPU programming in interactive fashion
picoGPT
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Understanding Automatic Differentiation in 30 lines of Python
In that case, you might also enjoy https://jaykmody.com/blog/gpt-from-scratch/
(here's the raw code: https://github.com/jaymody/picoGPT/blob/main/gpt2.py)
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Transformers from Scratch
I wrote a minimal implementation in NumPy here (the forward pass code is only 40 lines): https://github.com/jaymody/picoGPT
Although this is for a decoder-only transformer (aka GPT) and doesnt include the encoder part.
- FLaNK Stack Weekly 3 April 2023
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GPT-4 Says an Open-Source Chatbot Vicuna Reaches 90% ChatGPT Quality
Take a look at https://github.com/jaymody/picoGPT/blob/a750c145ba4d09d57648...
Yes, this is GPT-2 not 4 and it‘s not the Chat, only the model and it‘s basically only the inference part, not the training loop and it‘s somewhat simplified.
Still, take a good look.
That‘s essentially what it is and a single sheet of paper.
There is nothing specifically about language in „language model“, we just call it that. Better to call it just LLM.
Nobody knows exactly what it learns, although there would be ways to poke around given some research programs. But it seems like the interest in that is limited currently, everyone is busy with improving it or with applications.
Perhaps the answer is that we overestimated what a mind is. It‘s like we used to ask what life is and it turned out that there is nothing special about life, not even the DNA is controlling anything. It‘s merely a chemical process, even though a complex process.
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u/functor7 explains why AIs like ChatGPT do not "understand" their subject
(The hardest part was just designing a math function that has the capability of getting good at this game, but when all is said and done, it need not be a whole lot of code).
- PicoGPT: An unnecessarily tiny implementation of GPT-2 in NumPy
- picoGPT: An unnecessarily tiny implementation of GPT-2 in NumPy
What are some alternatives?
vscode-infracost - See cost estimates for Terraform right in your editor💰📉
gpt4all - gpt4all: run open-source LLMs anywhere
triton - Development repository for the Triton language and compiler
glances - Glances an Eye on your system. A top/htop alternative for GNU/Linux, BSD, Mac OS and Windows operating systems.
cutlass - CUDA Templates for Linear Algebra Subroutines
taskwarrior - Taskwarrior - Command line Task Management
carbon-lang - Carbon Language's main repository: documents, design, implementation, and related tools. (NOTE: Carbon Language is experimental; see README)
ctop - Top-like interface for container metrics
terraform-minecraft - A Terraform Script that can deploy Minecraft Servers
Tensor-Puzzles - Solve puzzles. Improve your pytorch.
owl - Owl - OCaml Scientific Computing @ https://ocaml.xyz
exiftool - ExifTool meta information reader/writer