NomicAI gpt4all AI technology Top Builders
Explore the top contributors showcasing the highest number of NomicAI gpt4all AI technology app submissions within our community.
GPT4All is an open-source ecosystem of on-edge large language models that run locally on consumer-grade CPUs. It offers a powerful and customizable AI assistant for a variety of tasks, including answering questions, writing content, understanding documents, and generating code.
GPT4All is supported and maintained by Nomic AI, which aims to make it easier for individuals and enterprises to train and deploy their own large language models on the edge.
|Type||Natural Language Processing|
Start building with GPT4All
To start building with GPT4All, visit the GPT4All website and follow the installation instructions for your operating system.
A curated list of libraries to help you build great projects with GPT4All.
- GPT4All Website
- GPT4All Documentation
- Python Bindings
- Typescript Bindings
- GoLang Bindings
- C# Bindings
For more information on GPT4All, including installation instructions, technical reports, and contribution guidelines, visit the GPT4All GitHub repository.
NomicAI gpt4all AI technology Hackathon projects
Discover innovative solutions crafted with NomicAI gpt4all AI technology, developed by our community members during our engaging hackathons.
Global News Explorer
Why live in a bubble constrained by language? Technology allows us to explore the world, gain insight and understanding from new perspectives… Russian politics news in Hindi Spanish Culture news in German German National news in English Japanese Business news in Portuguese No Problem! Welcome to a world where the boundaries of language no longer stand in the way of deeper connections, wherever humanity makes its mark. Our software creates a live audio stream based on contemporary topical news from around the world. Choose a language for the broadcast from a range including English, Hindi, Spanish, French, German, Italian, Polish and Portuguese. Choose a source country for your news then sit back and immerse yourself.
Our project aims to revolutionize the resume creation process by leveraging the power of AI Agents and web scraping. We understand that crafting a compelling resume can be a time-consuming and challenging task, and our tool is designed to simplify this process. By harnessing the capabilities of artificial intelligence and utilizing web scraping techniques, we have developed a custom resume creator that helps individuals create personalized resumes tailored to their unique skills, experiences, and career goals. Our AI Agents analyze user input, extract relevant information from online sources, and generate dynamic and industry-relevant content for each resume section. The result is a neatly formatted resume that stands out to potential employers and increases the chances of securing job opportunities. With user-friendly features, real-time editing options, and the ability to export and share resumes, our tool empowers users to confidently present their qualifications and achievements in a professional manner. Whether you're a seasoned professional or a career changer, our custom resume creator is here to support you in showcasing your potential and landing your dream job.
A trading agent AI is an artificial intelligence system that uses computational intelligence methods such as machine learning and deep reinforcement learning to automatically discover, implement, and fine-tune strategies for autonomous adaptive automated trading in financial markets This project implements a Stock Trading Bot, trained using Deep Reinforcement Learning, specifically Deep Q-learning. Implementation is kept simple and as close as possible to the algorithm discussed in the paper, for learning purposes. Generally, Reinforcement Learning is a family of machine learning techniques that allow us to create intelligent agents that learn from the environment by interacting with it, as they learn an optimal policy by trial and error. This is especially useful in many real world tasks where supervised learning might not be the best approach due to various reasons like nature of task itself, lack of appropriate labelled data, etc. The important idea here is that this technique can be applied to any real world task that can be described loosely as a Markovian process. This work uses a Model-free Reinforcement Learning technique called Deep Q-Learning (neural variant of Q-Learning). At any given time (episode), an agent abserves it's current state (n-day window stock price representation), selects and performs an action (buy/sell/hold), observes a subsequent state, receives some reward signal (difference in portfolio position) and lastly adjusts it's parameters based on the gradient of the loss computed. There have been several improvements to the Q-learning algorithm over the years, and a few have been implemented in this project: Vanilla DQN DQN with fixed target distribution Double DQN Prioritized Experience Replay Dueling Network Architectures Trained on GOOG 2010-17 stock data, tested on 2019 with a profit of $1141.45 (validated on 2018 with profit of $863.41):