This package makes it easy to train a VLM with GRPO.

Get your documents ready for gen AI

A one stop shop for data generation, fine tuning and testing LLMs locally using the best tools available. Keeping it simple and versatile!

The AI runtime that turns your framework functions into OpenAI compatible endpoints

Infinity is a high-throughput, low-latency serving engine for text-embeddings, reranking models, clip, clap and colpali

Gen-AI Chat for Teams - Think ChatGPT if it had access to your team's unique knowledge.

The easiest tool for fine-tuning LLM models, synthetic data generation, and collaborating on datasets.

Prompt Declaration Language (PDL) is a declarative prompt programming language.

Implementation of "BitNet: Scaling 1-bit Transformers for Large Language Models" in pytorch

Training Large Language Model to Reason in a Continuous Latent Space

Axiom Prompt Engineering testing and results

A 15TB Collection of Physics Simulation Datasets

Turn codebases into prompt-friendly text

Large-Scale Multimodal Dataset of Astronomical Data

A course on aligning smol models.

Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team.

PyTheus, a highly-efficient inverse-design algorithm for quantum optical experiments

A system for agentic LLM-powered data processing and ETL

Convert any PDF into a podcast episode!

Guides to hopefully simplify the process of using ROCm.

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and cont…

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design

Drop in a screenshot and convert it to clean HTML/Tailwind/JS code

This repository contains codes of Andrew Ng's course Machine learning

A collection of notes and implementations of machine learning algorithms from Andrew Ng's machine learning specialization.