OPEN KAITO is an open-source, decentralized platform designed to foster collaboration in AI-driven research and development. Built on a transparent, community-driven ethos, OPEN KAITO facilitates the collective building and sharing of machine learning models, datasets, and research insights through decentralized protocols.
The primary objective of Subnet 5 is to train and serve the best and most generalizable text-embedding models. Such text-embedding models can empower plenty of downstream applications such as semantic search, natural language understanding, and so on. Miners will be responsible for training models using an extensive corpus of textual data and serving the model in a low-latency and high-throughput way. These models will be utilized to generate high-quality embeddings for diverse text inputs. Validators will conduct rigorous evaluations of the models using multiple benchmarks. Performance comparisons will be made against existing SOTA text embedding models to ensure continuous improvement and competitiveness. Subnet users will gain access to cutting-edge text embedding models that are most generic and exceed SOTA performance. These models will be made publicly available through the validator API of Bittensor Subnet 5, facilitating widespread adoption and integration into various applications.
Roadmap
Phase 1
- The text-embedding model evaluation and incentive mechanism
- Subnet dashboard with model performance growing curve, and comparison to OpenAI text-embedding-3-small and text-embedding-3-large models as baselines
- Subnet API for serving the miners trained model to the subnet users.
V2 and further
- Extending the dataset
- Extending the evaluation incentive model to tasks like document re-ranking
- Incorporating the documents' pairwise distance in the evaluation
For more information, visit the OPEN KAITO GitHub Repository.