Decentralized LLM training / inference

Objective:
The objective is to deploy and fine-tune large language models (LLMs), such as Llama 3.2, on the NuNet platform using Ollama and OpenWebUI as key interfaces. The project aims to leverage NuNet's decentralized computing infrastructure for large-scale model training and inference, while exploring the use of small language models (SLMs) for efficient single GPU utilization.

Key Points:

• Leverage Ollama and OpenWebUI to facilitate the training, fine-tuning, and deployment of Llama 3.2 on NuNet’s decentralized infrastructure.
• Expose APIs through Ollama and OpenWebUI for decentralized, large-scale access to LLMs, with compatibility to OpenAI APIs for ease of integration.
• Explore the use of smaller language models (SLMs) to optimize performance and resource usage in single GPU scenarios.
• Investigate specialized LLMs tailored for specific tasks, such as codebase analysis, project-specific queries, and automated assistance for decentralized AI applications.

Deliverables:

• A strategy document detailing the deployment of Llama 3.2 (or similar LLMs) using Ollama and OpenWebUI on the NuNet platform.
• A working prototype demonstrating decentralized API access to fine-tuned LLMs through Ollama or OpenWebUI.
• A feasibility report on using SLMs for efficient single GPU utilization.
• Documentation for exposing LLMs via APIs over LibP2P, ensuring compatibility with widely-used APIs like OpenAI’s.

Timeline:

• Initial Research and Planning Phase: Duration to be decided.
• Prototype Development and Integration: Timeline dependent on research outcomes and infrastructure setup.

Additional Notes:

• Regular updates and comprehensive documentation are crucial to the project’s success.
• Collaboration with stakeholders, team members, and the NuNet community to ensure smooth deployment and optimization.
• Continuous evaluation of GPU resource efficiency when utilizing decentralized LLMs versus SLMs.
• Active exploration of decentralized AI tasks through Ollama and OpenWebUI interfaces.