Neural Condense Subnet
The subnet offers an additional benefit regarding privacy. If users or companies utilize a subnet to transform their context into condensed tokens before sending them to other LLM services, this approach can help prevent context leaks. The transformation increases the computational complexity, making it more difficult for unauthorized entities to extract the original context.
Key Features
- Seamless Integration - Effortlessly integrates with LLM inference engines, such as transformers 🤗, vllm.
- Token Compression - The subnet API compresses long sequences of natural language tokens into soft tokens.
- Decentralized Network - The subnet is a decentralized network that allows miners to contribute to the compression process.
- Tiered System - The subnet has a tiered system, with a research tier for experimentation and an inference tier for production-scale use. Incentive distribution is split for each tier.
- Benchmarking and Validation - The subnet owner defines synthetic metrics to benchmark miners’ performance, ensuring quality and efficiency.