SN37: Bittensor's New Subnet for Fine-Tuning
Fine-tuning delivers the 'final mile' of AI model development, playing a critical role in whether or not an AI application actually meets the needs of its users.
Enhancing Performance from the Start
Our core improvements will refine the codebase, removing inefficiencies and strengthening the subnet's design to increase competitiveness. We're also improving front-end experience by designing and launching a real-time leaderboard.
Driving Continuous Improvement as Standard
We'll also harden the incentive structure, by deploying incentive structures that prohibit miners from deploying models resistant to further improvements. This ensures all miners can use the leading model as a basis for further training, helping to maintain continuous improvement.
Deeper Integration for Bittensor
By layering through SN1's inference, SN9's pretraining, and SN13's data scraping, we can develop a feedback loop where data is sourced, refined, trained, and shared entirely within the Bittensor ecosystem. This can unlock entirely new use cases for the whole community.