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Starred repositories
Curated list of datasets and tools for post-training.
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
🏋️ A unified multi-backend utility for benchmarking Transformers, Timm, PEFT, Diffusers and Sentence-Transformers with full support of Optimum's hardware optimizations & quantization schemes.
The simplest, fastest repository for training/finetuning medium-sized GPTs.
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
This repository is for active development of the Azure SDK for Python. For consumers of the SDK we recommend visiting our public developer docs at https://learn.microsoft.com/python/azure/ or our v…
🦜🔗 Build context-aware reasoning applications
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
Examples and recipes around federated learning in Azure ML.
Code Repository for Machine Learning with PyTorch and Scikit-Learn
Lime: Explaining the predictions of any machine learning classifier
A game theoretic approach to explain the output of any machine learning model.
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
Resources and slides from the talk "Publishing (Perfect) Python Packages on PyPI"
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Public repository associated with "Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL"
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
A collection of New Grad full time roles in SWE, Quant, and PM.
An opinionated list of awesome Python frameworks, libraries, software and resources.
TensorFlow code and pre-trained models for BERT
Essential Cheat Sheets for deep learning and machine learning researchers https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5
Learn how to design, develop, deploy and iterate on production-grade ML applications.