Andrej Karpathy just released a new repo with an implementation of training LLM with pure C/Cude with a few lines of code 🚀. This repo, according to Andrej Karpathy, is still WIP, and the first working example is of GPT-2 (or the grand-daddy of LLMS 😅) 👇🏼
Hey, any Fedora folks out there ( also could include future Fedora people!) with involvement in upstream Pytorch and related development communities? #pytorch
A new crash course for getting started with #CUDA with #Python by Jeremy Howard 🚀. CUDA is NVIDIA's programming model for parallel computing on GPUs. CUDE is being used by tools such as #PyTorch#tensorflow and other #deeplearning and LLMs frameworks to speed up calculations. The course covers the following topics:
✅ Setting up CUDA
✅ CUDA foundation
✅ Working with Kernel
✅ CUDA with PyTorch
During the holidays, i.e. from the 23rd to the 7th, I will not work.
I'd like to continue playing with the #julialang and some #pytorch but it's really for pleasure.
I will read some stuff about brains and perception but it'll be Ed Yong's "An Immense World", and perhaps McCulloch and Ashby.
Also, I'm finishing Dune.
If you too have the "privilege" of taking a break, what are you up to?
JOSS publishes articles about open source research software. It is a free, open-source, community driven and developer-friendly online journal. JOSS reviews involve downloading and installing the software, and inspecting the repository and submitted paper for key elements
Please reach out if you are interested in reviewing this paper or know one who could review this paper.
Trying to live the single-(usbc)-cable-dream at work as well by going through my old thinkpad dock: mac to dock via usbc, dock to Delly U2713HM DisplayPort. However, DP to DP connection from dock to old Dell U2713HM display only sometimes flickers on and often not. usbc to DP from dock is solid. DP-DP cable is extremely sus. Work has ordered new DP-DP cable and I'm crossing my fingers. Singel cable life is fun.
Again discovered how unbelievably badly macOS renders fonts on resolutions that Apple believes to be too low, in this case my Dell Ultrasharp 27" at 2560x1440 aka QHD at work. Microsoft and Windows do an absolutely great job on exactly the same hardware, and fonts look great.
BTW, although macOS does marginally better on my 32" 4K Dell IPS display at home, here Windows even further increases its font rendering dominance with fractional scaling and cleartype.
As is often the case with Apple, there is a (paid) third-party software tool that works around their attempts to improve matters, namely BetterDisplay: https://github.com/waydabber/BetterDisplay
As I wrote in my notes some years ago when this became apparent, this is just the company's philosophy. They want to control all the hardware. They will begrudgingly let you use some third party displays, but they pick their battles to look good. In this case, it does feel quite user-hostile.
Ran into a M1-specific bug in the ruff vscode extension, where the arm64 extension build bundles the x86_64 ruff binary. Worked-around, and then reported at https://github.com/astral-sh/ruff-vscode/issues/364
I've been looking at Apple's MLX machine learning / array framework for Apple Silicon https://github.com/ml-explore/mlx as well as at CoreML because I'm curious whether this will give me faster Jina AI embeddings inference on the M1 than I'm currently getting with the PyTorch MPS backend, which is muuuuuch slower on this M1 Pro 10C / 16C GPU / 16C neural than PyTorch CUDA on my oooold GeForce RTX2070 with 8GB.
The PyTorch foundation released a few days ago gpt-fast - an example of a native #PyTorch code for transformer text generation 🚀. This example demonstrates a simple and efficient approach for running #GPT models with less than 1000 lines of #Python code, introducing the following features :
✅ Low latency ⏩
✅ Native PyTorch code - no other dependencies 🎯
✅ Supports Nvidia and AMD GPUs 😎
✅ Tensor parallelism ⌛️
Create a Large Language Model from Scratch with Python Tutorial 👇🏼
Another fun tutorial from freeCodeCamp, focusing on building LLM model from scratch with Python. It covers topics such as:
✅ Handling and processing text
✅ Core PyTorch functions for text
✅ Basic language models
✅ Advance methods
✅ Working with GPUs
PyTorch now supports AMD graphics cards as well (at least some select ones)!! This is huge news, because it means AI and ML development can now be pleasant on an AMD GPU as well! It even seems AMD is treating Linux as first class citizen on this one!
I only just today, for the first time, experienced @huggingface and their models, pipelines etc.
Completely new to all this, but holy heck. Having spent some time installing PyTorch and then running one of their pipelines I got my very first result back and holy heck.
I feel... all jittery :-D
And this was all for free. It seems... like it shouldn't be possible.
(1/2) Audiocraft is a new Python library for audio processing and generation with deep learning by Meta. It is based on PyTorch, and it provides AI generative models for producing high-quality audio and includes the following applications:
➡️ MusicGen: controllable text-to-music model.
➡️ AudioGen: text-to-sound model.
➡️ EnCodec: high fidelity neural audio codec.
➡️ Multi Band Diffusion: An EnCodec compatible decoder using diffusion.