Gradients (SN56)

Overview

AI training on Bittensor

Gradients is a platform for training and improving artificial intelligence models. It operates on Subnet 56, or SN56, of Bittensor, a blockchain network that rewards people for contributing to specialized AI tasks. Gradients focuses on making model training easier to start: a user can choose a model and dataset through a web interface or submit a job through an API. Its tools cover text and image models, while its subnet also runs competitions for training methods used with interactive AI agents. (gradients.io)

SN56 also has its own alpha token, the subnet-specific asset issued through Bittensor’s token system. The platform and token have different roles. The platform organizes training jobs and displays results; the token belongs to the network’s system for staking and rewarding participation. Understanding that connection is the key to understanding Gradients as a crypto project. (github.com)

History & Team

From launch to training tournaments

Gradients says it was founded in 2024 by WanderingWeights, an engineer who led its early decentralized group. The project registered as Bittensor Subnet 56 and launched its website in November 2024. Its published team profile names Besim as CEO and lead developer, WanderingWeights as founding engineer, and Samoline, Diagonalge, and Bonoliver as contributors. The platform is developed by Grads LLC. (gradients.io)

The early product centered on simple, on-demand fine-tuning. In February 2025, Gradients described text-training competitions and reported that the platform had handled more than 4,000 training jobs in its first weeks. It also introduced image training in a closed alpha. The subnet’s published code now describes recurring tournaments for text, image, and environment tasks, showing how the project has grown from a training interface into a competition for better training software. (gradients.io)

Technology & How It Works

Starting a training job

Most users begin with a base model—an AI system that already has general abilities—and a dataset containing examples for a more specific task. Gradients lets users select both, map the dataset’s columns to the fields a model needs, and start training. Its API offers another way to create jobs from software. Users can follow results on the platform, access more detailed monitoring through Weights & Biases, and view completed models through Hugging Face. (gradients.io)

This process is called fine-tuning. Rather than building a model from the beginning, it adjusts an existing one using new examples. A text model might learn the style of a set of instructions, while an image model might learn a visual concept from a collection of pictures. Gradients handles much of the job setup that would otherwise require a user to work directly with training software. (gradients.io)

Competition behind the interface

Beneath the user-facing tools is the Gradients on Demand, or G.O.D, subnet runtime. In its tournaments, participants called miners submit repositories containing their training code. They identify an exact version of that code so validators can run the same submission. Validators build and run the code on GPU infrastructure, compare the resulting models, and set network weights based on performance. In this setup, miners contribute training methods; validators provide and coordinate the computing resources used to test them. (github.com)

Text tournaments test approaches to tasks such as instruction tuning and preference-based training. Image tournaments compare methods for adapting image-generation models. Environment tournaments examine training for agents that act and receive feedback in interactive settings. Competition rounds narrow the field, and successful challengers face a defending champion. Winning training repositories are published for others to inspect and build upon. (github.com)

Tokenomics & Utility

The SN56 alpha token

Bittensor gives each subnet an alpha token with its own issuance schedule and a maximum supply of 21 million units. SN56’s alpha is specific to Gradients; TAO is the wider Bittensor network’s token. When someone stakes TAO toward a validator on SN56, Bittensor’s built-in pool exchanges that TAO for SN56 alpha held in the staking position. Reversing the operation exchanges the alpha back for TAO. (bittensor.com)

New subnet tokens enter the system through Bittensor’s emissions. At regular network intervals, alpha rewards are allocated among the subnet owner, miners, and validators and their stakers. On Gradients, validator weights reflect its training competitions, linking miner rewards to tournament results. The subnet’s share of wider Bittensor emissions is determined through network rules tied to demand for its alpha token; it is not a fixed allotment. (bittensor.com)

The token’s clearest established uses are subnet staking and participation rewards. Gradients’ published materials describe a separate payment process for customer orders through the reseller Paddle, while its tournament documentation describes participant fees in TAO. Those details help explain the economic model: SN56 alpha supports the Bittensor incentive system, while a training job is accessed through the Gradients service. (gradients.io)

Ecosystem & Use Cases

Models, developers, and open code

Gradients serves people who want to adapt a model to their own examples without managing every training step. Developers can create jobs through the API, and other users can work through the web interface. The site also displays community-made models, including image models trained on the platform. Completed models can be delivered through Hugging Face, giving users a familiar place to inspect or use the results. (gradients.io)

The ecosystem extends beyond the person submitting a job. Miners experiment with training strategies; validators run and score submissions; and open-source contributors can study published tournament winners. Gradients’ February 2025 research also described fine-tuning a model pretrained by another Bittensor subnet, illustrating how work from separate AI networks can be combined in a training workflow. (github.com)

Advantages & Challenges

Ease of use meets demanding evaluation

Gradients’ main advantage is its simple entry point. Choosing a model, choosing data, and launching a job through an interface takes less setup than building a training pipeline alone. Its API offers a route for developers who want to automate that process. Competition gives contributors a reason to test different methods, while published winning code makes successful approaches easier to examine. (gradients.io)

The harder work happens behind the interface. Validators must coordinate GPUs, run submitted code, and compare models across different tasks. Results can depend on the quality of the starting model, the dataset, and the method being tested. The service also has clear data-handling terms: users retain ownership of uploaded data, Gradients says it stores training data for one year after a job, and delivered Hugging Face repositories are public by default. These details matter when choosing what data to train on and how to share a finished model. (github.com)

Where to Buy & Wallets

Accessing SN56 through TAO

SN56 alpha can be acquired through Bittensor’s native subnet staking pool using TAO. TAO is available on Kraken and, in supported regions, Binance. After moving TAO to a Bittensor wallet, a user can stake it toward a validator on netuid 56 to receive an SN56 alpha staking position. Bittensor’s command-line tools support both staking and unstaking through the subnet pool. (kraken.com)

Bittensor’s wallet uses a coldkey to hold funds and sign staking transactions. A hotkey identifies a participant’s operational role on a subnet. The Bittensor CLI can create a wallet, show balances, and display positions across subnets. Together, these tools provide a direct way to hold TAO and manage an SN56 stake on the network where the token is issued. (preview.bittensor.com)

Regulatory & Compliance

Digital-asset rules and shariah assessment

SN56’s legal treatment depends on the activity and jurisdiction involved. In the United States, the Securities and Exchange Commission explains that securities rules can apply to crypto-asset transactions according to their features and how they are offered. The Internal Revenue Service treats reportable digital-asset activity—including certain rewards, sales, and exchanges—as part of the tax system. In the European Union, the Markets in Crypto-Assets framework sets requirements for covered public offers, trading admission, and crypto-asset services. (sec.gov)

A project-specific halal or shariah-compliance determination for SN56 has not been established in Gradients’ published materials. Shariah assessment examines more than a token’s name or its use of blockchain. For example, Malaysia’s Securities Commission Shariah Advisory Council evaluates digital tokens by looking at the purposes of their issuance proceeds and the rights and benefits attached to them. That framework applies within its stated jurisdiction; it provides useful context for the questions an assessment of SN56 would need to address. (gradients.io)

Future Outlook

Improving the training network

Gradients’ development can be followed through the work its tools and tournaments already support: making training jobs easier to submit, improving how models are evaluated, and publishing effective methods. Its text, image, and environment competitions give contributors several kinds of problems to solve rather than a single benchmark. The project’s value as an AI service will depend on whether those methods continue to produce useful models for the people who submit jobs. (gradients.io)

SN56 also remains part of Bittensor’s broader subnet economy. Its alpha token connects staking and rewards to a network devoted to AI training. As the platform develops, the relationship between accessible tools for users and strong competition among contributors will remain central to its design. (bittensor.com)

Summary

Gradients brings together an easy-to-use AI training platform and a Bittensor subnet that rewards contributors for better training methods. Users can fine-tune text and image models through an interface or API, while miners and validators compete and evaluate approaches behind the scenes. SN56 alpha supports staking and rewards within that subnet, giving Gradients a distinct place in the crypto ecosystem’s efforts to coordinate useful AI work. (gradients.io)