Gradients is founded by Chris, known in the Bittensor community as Wandering Weights. Development and operations are supported by Rayon Labs. The project is fully open-source, with code maintained under the gradients-ai GitHub organization.
What is Gradients?
Gradients is a decentralized AI training platform that enables users to fine-tune and deploy models directly through a browser interface - no complex infrastructure or deep ML knowledge required.Gradients makes it easy to customize language, vision, and multimodal models using your own datasets. It integrates with tools like Weights & Biases and Hugging Face, giving users full control and visibility over training workflows.
Gradients is ideal for ML researchers, product teams, and hobbyists who want to build production-ready models in a fast, scalable, and decentralized way.
How Gradients works?
The current version of Gradients operates through a tournament system. Miners submit open-source training scripts that are executed by validators on dedicated infrastructure. Each tournament lasts 4 to 7 days, with new tournaments starting 72 hours after the previous one ends. Validators provide fixed compute, run all submitted scripts, and compare results head-to-head. The top-performing miners receive exponentially higher weight and emissions. Winning AutoML scripts are released publicly to the gradients-opensource GitHub organization, building an open library of training techniques.
Rather than using a single predetermined strategy to fine-tune a model (the approach taken by centralized platforms like Google Cloud AutoML or HuggingFace AutoTrain), Gradients pools multiple miners who each work independently to discover the best fine-tuning configuration for a given dataset and task. This competitive approach means the platform consistently finds configurations that a single automated pipeline would miss. Miners are evaluated on loss scores measured on held-out test data they never access during training, ensuring genuine generalization rather than overfitting.
