Popular GitHub Repositories for Bare Metal

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Filament是一个实时物理渲染引擎，支持Android、iOS、Windows、Linux、macOS和WebGL2平台。

Open deep learning compiler stack for cpu, gpu and specialized accelerators

Simple, scalable AI model deployment on GPU clusters

Bare bone examples of machine learning in TensorFlow

Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.

Run AI models locally on your machine with node.js bindings for llama.cpp. Enforce a JSON schema on the model output on the generation level

:metal: TT-NN operator library, and TT-Metalium low level kernel programming model.

Run LLaMA (and Stanford-Alpaca) inference on Apple Silicon GPUs.

Code for testing various M1 Chip benchmarks with TensorFlow.

Source code for my blog post "Getting started with TensorFlow on iOS"

Run larger LLMs with longer contexts on Apple Silicon by using differentiated precision for KV cache quantization. KVSplit enables 8-bit keys & 4-bit values, reducing memory by 59% with <1% quality loss. Includes benchmarking, visualization, and one-command setup. Optimized for M1/M2/M3 Macs with Metal support.

TensorFlow Metal Backend on Apple Silicon Experiments (just for fun)

Phi-3.5 for Mac: Locally-run Vision and Language Models for Apple Silicon

A bare-bones Python library for quality diversity optimization.

A few quick scripts focused on testing TensorFlow/PyTorch/Llama 2 on macOS.

ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

A very simple, bare-bones, inefficient, implementation of skip-gram word2vec from scratch with Python

Photo Assessment using Core ML and Metal.