Tool for training large AI models
Higgsfield is a machine learning framework designed to train models with billions and trillions of parameters. It supports large-scale AI projects with fault-tolerant and highly scalable graphics processing unit (GPU) orchestration capabilities.
Updates
- September 27, 2026: Stars 5,541 → 5,764, latest release v0.0.4-rc (March 23, 2024).
What you get
- Trains models with billions of parameters
- Manages graphics processing unit resources
- Facilitates distributed training processes
Installation
pip install higgsfield==0.0.3If you don't write code
To train large AI models and manage resources, you can install the library by running the pip install higgsfield==0.0.3 command in your terminal and start using it in your project.
Repository: https://github.com/higgsfield-ai/higgsfield. Follow the official instructions in the README of this repository for setup and execution.
Before starting, check which OS we are on and if the necessary tools (e.g., Python, Node.js, Git, Docker) are installed. Install any missing ones step-by-step according to this system.
Create a new, empty folder to work in. Do not touch any files outside this folder and do not delete anything. Tell me what you are going to do in one sentence before running each command.
Tell me what I should see on the screen at the end of each step. If an error occurs, I will paste the error message to you exactly as it is, and we will fix it together.
Related dictionary terms
Links
TreScout did not build this tool · we found it in GitHub trends and wrote it up. This page describes the repository as of 2026-09-20: The star count and our text belong to that day, the repository may have changed since. Check the repository link for the current state. The introductory text was prepared with AI from the repository's own documentation, and this page was machine-translated from the Turkish original · the Turkish version prevails.