gitroom
tinygrad
| gitroom | tinygrad | |
|---|---|---|
| 13 | 17 | |
| 3,242 | 24,138 | |
| 8.6% | 3.8% | |
| 9.1 | 10.0 | |
| 4 days ago | 3 days ago | |
| TypeScript | Python | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
gitroom
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React Crazy: This AI library transformed my app to the next level
(it's open-sourced 🙏🏻) That's nice and all, but here is the real kicker.
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Why Do I Love Open Source?
We must be careful that the value, worth, and success of open-source projects are not measured by vanity metrics such as stars on GitHub or attempts at gaming the GitHub trending algorithm. For one thing, not all open source worth investment happens on GitHub, there are also platforms such as GitLab, Codeberg, and BitBucket where a lot of great work is being done. Some people also overblow the success of a project by purchasing social media accounts and carefully curating actions to ensure their project trends or goes viral. To me, this is a race to the bottom where everyone loses.
- Gitroom: Schedule Social Media and Articles
- Show HN: Gitroom – schedule social media and articles
- Show HN: Schedule your open-source launch week
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💻 7 Open-Source DevTools That Save Time You Didn't Know to Exist ⌛🚀
🌟 Support on GitHub Website: https://clickvote.dev/
- Show HN: Clickvote – Open-source upvotes, likes, and reviews to any context
- Clickvote: Open-source upvotes, likes, and reviews to any context
- Show HN: Clickvote Open-source upvotes likes and reviews to any context
tinygrad
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AMD Unveils Ryzen 8000G Series Processors: Zen 4 APUs for Desktop with Ryzen AI
Not sure if I completely understand what "Ryzen AI" does, but Tinygrad for example has some limited support for RDNA3[0]. It isn't quite there yet in matters of performance though, as you can read in the comments of that file.
There's also a small tutorial by AMD on how to use the WMMA intrinsic[1] using AMD's hipcc[2] compiler. Documentation is sparse kinda sparse, but the instruction set is not huge. The RDNA3 ISA guide[3] might also be helpful (and only a fraction of the pages are relevant.)
0. https://github.com/tinygrad/tinygrad/blob/master/extra/gemm/...
1. https://gpuopen.com/learn/wmma_on_rdna3/
2. https://github.com/ROCm/HIPCC
3. https://www.amd.com/content/dam/amd/en/documents/radeon-tech...
- Tinygrad 0.8.0 Release
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Beyond Backpropagation - Higher Order, Forward and Reverse-mode Automatic Differentiation for Tensorken
This post describes how I added automatic differentiation to Tensorken. Tensorken is my attempt to build a fully featured yet easy-to-understand and hackable implementation of a deep learning library in Rust. It takes inspiration from the likes of PyTorch, Tinygrad, and JAX.
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[D] What is a good way to maintain code readability and code quality while scaling up complexity in libraries like Hugging Face?
what do you think about tinygrad? I think its a good example of growing and well written, (partially) well documented library with many close to reference implementations
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AMD MI300 Performance – Faster Than H100, but How Much?
The idea of model architecture making fast hardware design easier is what makes https://github.com/tinygrad/tinygrad so interesting.
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💻 7 Open-Source DevTools That Save Time You Didn't Know to Exist ⌛🚀
🌟 Support on GitHub Website: https://tinygrad.org/
- Tinygrad
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How to train an Iris dataset classifier with Tinygrad
Before we begin, make sure you have TinyGrad and the required dependencies installed. You can find the installation instructions here.
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Decomposing Language Models into Understandable Components
Try to get something like tinygrad[1] running locally, that way you can tweak things a bit run it again and see how it performs. While doing this you'll pick up most of the concepts and get a feeling of how things work. Also, take a look at projects like llama.cpp[2], you don't have to fully understand what's going on here, tho.
You may need some intermediate knowledge of linear algebra and this thing called "data science" nowadays, which is pretty much knowing how to mangle data and visualize it.
Try creating a small model on your own, it doesn't have to be super fancy just make sure it does something you want it to do. And then ... you'll probably could go on your own then.
1: https://github.com/tinygrad/tinygrad
2: https://github.com/ggerganov/llama.cpp
- Tinygrad 0.7.0
What are some alternatives?
postgres_exporter - A PostgreSQL metric exporter for Prometheus
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
llama.cpp - LLM inference in C/C++
llama - Inference code for Llama models
openpilot - openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for 250+ supported car makes and models.
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
stable-diffusion.cpp - Stable Diffusion in pure C/C++
llama-mps - Experimental fork of Facebooks LLaMa model which runs it with GPU acceleration on Apple Silicon M1/M2
hlb-CIFAR10 - Train CIFAR-10 in <7 seconds on an A100, the current world record.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
docs - Hardware and software docs / wiki