SimpleTuner is geared towards simplicity, with a focus on making the code easily understood. This codebase serves as a shared academic exercise, and contributions are welcome.

In a nutshell, we aim to generate polyphonic music of multiple tracks (instruments). The proposed models are able to generate music either from scratch, or by accompanying a track given a priori by the user.

This is a port of BlinkDL/RWKV-LM to ggerganov/ggml. Besides the usual FP32, it supports FP16, quantized INT4, INT5 and INT8 inference.

Version, test, and monitor every prompt and agent with robust evals, tracing, and regression sets. --- This library provides convenient access to the PromptLayer API from applications written in python.

Website • Docs • Community Slack NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, interactive visualizations, is completely model-agnostic and currently supports all tabular use cases, classification and regression.

This repository is the reading list on Deep Learning for Mathematical Reasoning (DL4MATH). Contributors: Pan Lu @UCLA, Liang Qiu @UCLA, Wenhao Yu @Notre Dame, Sean Welleck @UW, Kai-Wei Chang @UCLA For more details, please refer to the paper: A Survey of Deep Learning for Mathematical Reasoning.

AlphaSuite is an open-source quantitative analysis platform that gives you the power to build, test, and deploy professional-grade trading strategies. It's designed for traders and analysts who want to move beyond simple backtests and develop a genuine, data-driven edge in the financial markets.

AI Audio Datasets (AI-ADS) 🎵, including Speech, Music, and Sound Effects, which can provide training data for Generative AI, AIGC, AI model training, intelligent audio tool development, and audio applications.

𝕏 Follow me on X • 🤗 Hugging Face • 💻 Blog • 📙 LLM Engineer's Handbook The LLM course is divided into three parts: 1. 🧩 LLM Fundamentals is optional and covers fundamental knowledge about mathematics, Python, and neural networks.

This newer video covers the an updated 2024 version of the state of MLOps. You can join the Machine Learning Engineer newsletter.

Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible but rather to present the inner workings of them in a transparent and accessible way.

This is a hands-on guide to machine learning for programmers with no background in AI. Using a neural network doesn’t require a PhD, and you don’t need to be the person who makes the next breakthrough in AI in order to use what exists today.

Awesome Prompt Engineering 🧙‍♂️ A hand-curated collection of resources for Prompt Engineering and Context Engineering — covering papers, tools, models, APIs, benchmarks, courses, and communities for working with Large Language Models. https://promptslab.github.io --- New to prompt engineering?