Popular GitHub Repositories for Evoked Potential

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

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The paper list of the 86-page paper "The Rise and Potential of Large Language Model Based Agents: A Survey" by Zhiheng Xi et al.

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

Official repo for "Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models"

A deep learning package for many-body potential energy representation and molecular dynamics

LLM-PowerHouse: Unleash LLMs' potential through curated tutorials, best practices, and ready-to-use code for custom training and inferencing.

A powerful tool for automated LLM fuzzing. It is designed to help developers and security researchers identify and mitigate potential jailbreaks in their LLM APIs.

Accurate Neural Network Potential on PyTorch

???♂? Library designed for developers eager to explore the potential of Large Language Models (LLMs) and other generative AI through a clean, effective, and Go-idiomatic approach.

The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field

Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov

Code of Paper "Joint Task Offloading and Resource Optimization in NOMA-based Vehicular Edge Computing: A Game-Theoretic DRL Approach", JSA 2022.

Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2

Neural Scene Flow Fields using pytorch-lightning, with potential improvements

ANI-1 neural net potential with python interface (ASE)

Immersive workshop showcasing the remarkable potential of integrating SoTA foundation models to enhance product experiences and streamline backend workflows. Leverages Microsoft's Copilot stack, Semantic Kernel and Azure primitives to offer an engaging and comprehensive introduction to AI-infused app development and deployment

To simplify and streamline LLM operations, empowering developers and organizations to harness the full potential of large language models with ease.

DrugGPT: A GPT-based Strategy for Designing Potential Ligands Targeting Specific Proteins

Atoms In Molecules Neural Network Potential