ML Frameworks

It wraps local inference and fine-tuning for open models in a web UI, using custom kernels to squeeze more performance out of desktop GPUs than standard tooling.

It teaches how LLMs work by implementing tokenization, attention, pretraining, and finetuning in pure PyTorch, one notebook at a time.

It centralizes model definitions so the same architecture works across PyTorch, JAX, vLLM, and llama.cpp without rewrites.

It exists to handle the tedious wiring—annotations, dataset formats, tracking—that sits between a trained model and a useful application.

Ultralytics wants to stop you from stitching together separate repos for every computer vision task by bundling detection, segmentation, tracking, and pose estimation into one YOLO-backed package.

A rewrite of minGPT that prioritizes working, hackable training code over educational scaffolding.

nanochat is a minimal, hackable harness that lets you train and chat with a GPT-2-class LLM on a single GPU node for under $100—no hyperparameter spreadsheets required.

PyTorch exists to give researchers and engineers GPU-accelerated tensor math and automatic differentiation without forcing them to leave Python’s debugger and stack traces behind.

A PyTorch toolkit that treats robot learning like Hugging Face treats NLP: standardized datasets, pretrained policies, and one interface for many arms.

Google's attempt to own the full machine-learning stack, from research lab to Raspberry Pi.

TimesFM is a pretrained decoder-only transformer that turns historical sequences into point and quantile forecasts without training from scratch.

It exists because keeping up with the training loops, quantization tricks, and inference stacks of 100+ models is a full-time job most developers would rather delegate.

It exists to let developers run customized vision, text, and audio machine learning across mobile, web, and edge hardware without cloud round-trips.

exo auto-discovers Apple devices on your network and shards frontier models across them, turning a pile of Macs into a single inference engine.

Jupyter notebooks that prove you can write a GPT with little more than high-school calculus and stubbornness.

Ray treats distributed computing as a Python primitive, then layers on libraries for training, tuning, serving, and reinforcement learning.

A readable reference for how classic machine learning actually works under the hood, from backprop to genetic algorithms.

Because training a transformer shouldn't require 245MB of PyTorch just to multiply matrices.

A volunteer-maintained Python machine learning module built on SciPy since 2007.

DeepSpeed is the optimization library that let the BLOOM and MT-530B teams train models too large to fit in any single GPU.