M

Machine Learning

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

huggingface.co/transformers https://github.com/huggingface/transformers

https://github.com/Lightning-AI/pytorch-lightning 🔗 lightning.ai Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.

https://github.com/roboflow/roboflow-python The official Roboflow Python package. Manage your datasets, models, and deployments. Roboflow has everything you need to build a computer vision application.

🔧🔗https://github.com/HumanSignal/label-studio-ml-backend

Configs and boilerplates for Label Studio's Machine Learning backend

https://github.com/Lightning-AI/torchmetrics Torchmetrics - Machine learning metrics for distributed, scalable PyTorch applications. 🔗 https://lightning.ai/docs/torchmetrics/

MiroTrain

https://github.com/MiroMindAI/MiroTrain

MiroTrain is an efficient and algorithm-first framework research agent.

🔧🔗https://github.com/MinishLab/model2vec

The Fastest State-of-the-Art Static Embeddings in the World

🔧🔗https://github.com/microsoft/Trace End-to-end Generative Optimization for AI Agents

🔧🔗https://github.com/andrewkchan/yalm

Yet Another Language Model: LLM inference in C++/CUDA, no libraries except for I/O

🔧🔗https://github.com/MinishLab/tokenlearn

Pre-train Static Word Embeddings

https://github.com/Lightning-AI/tutorials Collection of Pytorch lightning tutorial form as rich scripts automatically transformed to ipython notebooks. 🔗 https://lightning-ai.github.io/tutorials

https://github.com/Lightning-AI/lightning-Habana Lightning support for Intel Habana accelerators.

🔧🔗https://gitlab.com/gitlab-org/modelops/mlops/gitlab-mlops

A Python client for integrating GitLab's MLOps features, specifically designed to leverage GitLab's experiment tracking and model registry capabilities. This client simplifies the management of machine learning workflows by combining MLflow's flexibility with GitLab's powerful MLOps tools.

Reasoning Computers. Lambda Calculus, Fully Differentiable. Also Neural Stacks, Queues, Arrays, Lists, Trees, and Latches.

https://github.com/coqui-ai/Trainer 🐸 - A general purpose model trainer, as flexible as it gets

https://github.com/coqui-ai/coqpit Simple but maybe too simple config management through python data classes. We use it for machine learning.

https://github.com/silv-io/mulambda A Serverless Platform for Automated Machine Learning