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Bittensor aims to democratize the process of building AI-driven use cases by creating an open P2P marketplace where people can share and utilize machine learning models. The key idea behind Bittensor is that it forms an interconnected machine intelligence neural network. Anyone can leverage this network and build subnets, which are dedicated protocols that use collective intelligence to support various AI projects. Currently, there are 32 subnets in the Bittensor ecosystem, each focusing on a unique use case. Think applications like speech-to-text, image generation, AI-driven search engines, advanced trading strategies, or even fine-tuning large language models (LLMs) for other use cases. The possibilities are endless.
For example, some insights in this article come from Corcel, which is a user-friendly tool in Subnet 18 of the Bittensor ecosystem (also known as the Cortex.t subnet). Its functionality is similar to chatGPT, but it uses the collective intelligence of Bittensor's network of machine learning models to provide optimal responses to user queries.
Now, let's take a look at some prominent subnets and their functionalities within the Bittensor ecosystem:
Subnet 6, operated by the renowned Nous Research team, stands out in the Bittensor ecosystem. This subnet specializes in fine-tuning large language models (LLMs) using synthetic data from Corcel in Subnet 18.
Every miner in Subnet 6 receives the same synthetic data daily and uses it to fine-tune LLMs for specific outcomes. They employ their own strategies and techniques to achieve optimal performance on this data.
A key competitive element is the TAO rewards. Miners with lower "head-to-head loss" (meaning they make fewer mistakes) can earn more TAO rewards. This incentivizes everyone to do their best and continuously improve their models to climb the fine-tuning subnet leaderboard. |
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Post time 14-4-2024 20:32:25