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joellidin merged 2 commits into from
Jan 21, 2026
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(docs) Add burn period notice to validator guide #690

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(docs) Add burn period notice to validator guide
joellidin Jan 21, 2026
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(docs) Add burn period notice to validator guide (#689)
joellidin Jan 21, 2026
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8 changes: 8 additions & 0 deletions README.md
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Expand Up @@ -19,6 +19,14 @@ Documentation: <a href="https://github.com/one-covenant/templar/blob/main/docs/m
<a href="https://github.com/one-covenant/templar/actions/workflows/ci.yml"><img src="https://github.com/one-covenant/templar/actions/workflows/ci.yml/badge.svg" alt="CI" style="border: none;" /></a><a href="https://codecov.io/gh/one-covenant/templar"><img src="https://codecov.io/gh/one-covenant/templar/branch/main/graph/badge.svg" alt="Codecov" style="border: none;" /></a><a href="https://github.com/one-covenant/templar/blob/main/LICENSE"><img src="https://img.shields.io/github/license/one-covenant/templar" alt="License" style="border: none;" /></a><a href="https://github.com/one-covenant/templar/commits/main"><img src="https://img.shields.io/github/last-commit/one-covenant/templar" alt="Last Commit" style="border: none;" /></a><a href="https://deepwiki.com/one-covenant/templar"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki" style="border: none;" /></a><a href="https://wandb.ai/tplr/templar"><img src="https://img.shields.io/badge/W%26B-Public%20Dashboard-FFBE00?logo=weightsandbiases" alt="Weights & Biases" style="border: none;" /></a><a href="https://grafana.tplr.ai/d/ceia6bwlwn8qof/eval-metrics"><img src="https://img.shields.io/badge/Grafana-Eval%20Metrics-F46800?logo=grafana&logoColor=white" alt="Grafana Dashboard" style="border: none;" /></a>
</p>

> **⚠️ 100% Burn Period Active (January 2026)**
>
> **Templar is currently in a 100% burn period. Miners are not being evaluated and no emissions are being distributed.**
>
> The Covenant72B training run is complete - the largest model trained on decentralized infrastructure (72B parameters).
>
> We are now finalizing **Templar: Crusades**, a competition system where you submit training code, we evaluate it on target hardware, and the fastest implementation earns emissions. Full details coming soon.
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## Introduction

τemplar is a decentralized training framework that enables large-scale model training across heterogeneous compute resources over the internet. By connecting diverse computational nodes through a carefully designed incentive mechanism, τemplar makes it possible to train large models collaboratively while ensuring honest participation and quality contributions.
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# Templar Documentation

> **⚠️ 100% Burn Period Active**: Miners are not being evaluated and no emissions are being distributed. See the main [README](../README.md) for current status and information about Templar: Crusades.

Welcome to the Templar documentation. This index provides an overview of all available documentation to help you navigate and find the information you need.

## 📚 Table of Contents
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# Miner Setup

> **⚠️ 100% Burn Period Active**: Miners are not being evaluated and no emissions are being distributed. The Covenant72B training phase is complete. **Templar: Crusades** - a new competition system - is coming soon. See the main [README](../README.md) for current status.
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This document provides a comprehensive guide on how to set up and run a miner using `miner.py`. Miners are crucial components of **τemplar**, responsible for training the model on assigned data subsets and sharing their gradients with peers.

## Table of Contents
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2 changes: 2 additions & 0 deletions docs/validator.md
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# Validator Setup

> **⚠️ 100% Burn Period Active**: The Covenant72B training phase is complete. Validators are burning 100% of emissions (see [`neurons/burn.py`](../neurons/burn.py)) and are not evaluating miner gradients. **Templar: Crusades** information coming soon. See the main [README](../README.md) for current status.
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This document provides a comprehensive guide on how to set up and run a validator using `validator.py`. Validators are crucial components of **τemplar**, responsible for evaluating miners' contributions by assessing their uploaded gradients.

## Table of Contents
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