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Releases: LogicNet-Subnet/LogicNet

v1.0.6

12 Sep 22:27
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What's Changed

  • Update load state by @vietbeu in #18
  • update Bittensor Patch version regrading the bittensor breaking update
  • Reduce likelihood of correctness hijacking. by @jondurbin in #19

New Contributors

Full Changelog: v1.0.5...v1.0.6

v1.0.5

02 Sep 17:18
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Full Changelog: v1.0.4...v1.0.5

v1.0.4-fix

23 Aug 07:51
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What's Changed

  • Update pipeline and docs for running different model by @LVH-Tony in #12
  • fix weight and update patch to address breaking change by @LVH-Tony in #13

Full Changelog: v1.0.3...v1.0.4-fix

v1.0.4-broken

22 Aug 19:25
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  • Update pipeline and docs for running different model by @LVH-Tony in #12

Full Changelog: v1.0.3...v1.0.4

v1.0.3

02 Aug 16:03
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🚀 New Features

  • Enhanced API Handling: Added a new function to handle information from API calls, including re-validation of validators and improved request processing.

🔧 Improvements

  • Miner Information Management: Improved score rounding and retention of recent scores, with a new method for easy data conversion.

  • Validator Enhancements: Introduced a feature to post miner information asynchronously, ensuring smooth operation and real-time updates without blocking other processes.

🐛 Bug Fixes & Optimization

  • Security Enhancements: Refined token authentication and credential verification processes for better security.

  • Performance Optimization: Enhanced the efficiency of handling requests and scoring responses for API requests.

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Full Changelog: v1.0.2...v1.0.3

v1.0.2

25 Jul 17:00
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v1.0.1

20 Jul 13:55
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Full Changelog: v1.0.0...v1.0.1

v1.0.0

18 Jul 17:55
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🧠 LogicNet - Subnet 🤖

Introduction

Description

LogicNet-Subnet is a decentralized network utilizing the Bittensor protocol for advanced AI/ML model development focused on mathematics, computational thinking, and data analysis. This document guides validators and miners on setup, operation, and contribution.

Key Features

  • 🚀 Advanced Computational Network: Incentivizing miners to enhance computational resources for complex AI/ML tasks.
  • 📈 Performance Commitment: Miners commit to a category and volume per epoch.
  • 💰 Incentive Mechanism:
    • reward = (0.6 * accuracy_score) + (0.4 * reasoning_score) - 0.1 * time_penalty
    • accuracy_score is based on the correctness of the result.
    • reasoning_score measures the similarity between the generated text and reference data.
    • time_score is derived from the time taken to generate the result.
  • 🌟 Continuous Improvement: Adding more math problem set and category.

Neurons Documentation

New Contributors

Full Changelog: https://github.com/LogicNet-Subnet/LogicNet/commits/v1.0.0