tinygrad

This may not be the best deep learning framework, but it is a deep learning framework.

Due to its extreme simplicity, it aims to be the easiest framework to add new accelerators to, with support for both inference and training. If XLA is CISC, tinygrad is RISC.

Features:

• LLaMA and Stable Diffusion: tinygrad can run LLaMA and Stable Diffusion!
• Laziness: Try a matmul. See how, despite the style, it is fused into one kernel with the power of laziness.
• Neural networks: As it turns out, 90% of what you need for neural networks are a decent autograd/tensor library. Throw in an optimizer, a data loader, and some compute, and you have all you need.