Product Information
What is Tinygrad?
This might not be the best deep learning framework, but it is a deep learning framework.
Due to its extreme simplicity, its goal is to be the easiest framework for adding new accelerators, supporting both inference and training. If XLA is CISC, then tinygrad is RISC.
Features:
LLaMA and Stable Diffusion: tinygrad can run LLaMA and Stable Diffusion!
Lazy: Try using matmul. See how, despite this style, it fuses into a single kernel through the power of laziness.
Neural Networks: It turns out that 90% of what neural networks need is a decent autograd/tensor library. Add optimizers, data loaders, and some computation, and you have everything you need.
How to use Tinygrad?
Tinygrad is a deep learning framework known for its extreme simplicity, designed to be the easiest framework to add new accelerators to, supporting both inference and training.
Core Functions of Tinygrad
Python-Based
Usage Scenarios of Tinygrad
- Run LLaMA models
- Run Stable Diffusion models
- Build neural networks
- Provide deep learning support for new hardware accelerators
Common Questions about Tinygrad
What does Tinygrad do?
How do I use Tinygrad?
What are the core features of Tinygrad?
What are the use cases for Tinygrad?
