Title: Basilisk: A Self-Improving LLM Leveraging Distributed Proof of Work and Blockchain Technology
Abstract:
Basilisk is a novel self-improving Large Language Model (LLM) that leverages the power of distributed Proof of Work (PoW) and blockchain technology to incentivize improvements in a decentralized manner. This whitepaper outlines the core components of Basilisk, including the model architecture, the PoW distribution algorithm, and the integration of Nvidia CUDA or OpenCL for enhanced performance. By rewarding nodes with BasiliskCoin, the system promotes a decentralized and efficient approach to improving the LLM, ultimately benefiting users and stakeholders alike.
Introduction
Recent advancements in artificial intelligence have led to significant improvements in LLMs, with applications ranging from natural language processing to data analysis. However, these models rely heavily on centralized infrastructure and GPU resources, posing challenges in terms of scalability and efficiency. Basilisk aims to address these limitations by distributing the workload across a network of nodes, incentivizing participation through the issuance of BasiliskCoin.
Basilisk LLM Architecture
The Basilisk LLM is built on a transformer-based architecture, similar to GPT-4. However, the model's primary distinction lies in its decentralized and self-improving nature, enabled through a PoW-based blockchain.
Distributed Proof of Work (PoW) Model
Basilisk employs a PoW model to distribute work units across the network, effectively decentralizing the task of improving the LLM. This model relies on a consensus mechanism where each node solves a complex mathematical problem to validate and add new blocks to the blockchain. The PoW algorithm also ensures fairness, as the probability of solving the problem is directly proportional to the node's computational power.
Integration of Nvidia CUDA and OpenCL
To further optimize performance, Basilisk incorporates support for both Nvidia CUDA and OpenCL, allowing nodes to harness their GPU resources efficiently. This integration enables the model to run on a wide range of hardware configurations, making it more accessible to participants.
Incentivization with BasiliskCoin
To encourage nodes to contribute to the improvement of the LLM, Basilisk employs a native digital currency, BasiliskCoin. Nodes that successfully validate and add a new block to the blockchain are rewarded with 25 BasiliskCoins, effectively incentivizing participation and continuous improvement of the LLM.
Threshold-based Reward Mechanism
Basilisk employs a threshold-based reward mechanism, where nodes must reach a predetermined level of contribution to the model's improvement before receiving rewards. This approach ensures that only meaningful and impactful contributions are rewarded, promoting the overall quality of the LLM.
Conclusion
Basilisk offers a groundbreaking approach to LLM development, harnessing the power of distributed PoW and blockchain technology to decentralize and incentivize model improvement. By leveraging Nvidia CUDA or OpenCL and rewarding nodes with BasiliskCoin, the project aims to create a more efficient, scalable, and accessible AI ecosystem. As Basilisk continues to evolve and improve, it has the potential to revolutionize the way LLMs are developed, deployed, and maintained.