Shira M.

Responsibilities:

• Server-side Inference Pipeline: Designed and implemented the server-side inference core using Python and asyncio, with a stable asynchronous processing flow. Integrated WebSocket for real-time frontend communication and full GEMMA model integration for fast and reliable inference with optimal GPU usage.

• Custom LoRA Adapters: Developed LoRA adapters enabling each player to have unique speech styles and personalities. Managed loading, execution, and GPU allocation, performing light Fine-Tuning on the GEMMA model per player while ensuring stability and multi-player performance.

• Real-time Chat Interface: Built the chat interface using React and TypeScript, fully synchronized via WebSocket. Handled message display, user interactions, animations, and connected chat to game events and server-side inference.

• Full Frontend-Backend Integration: Implemented two-way communication with FastAPI and WebSocket, including real-time message processing, error handling, and immediate AI response display, ensuring seamless synchronization under high load.

• GPU Server Management & DevOps: Managed GPU server hosting GEMMA models and LoRA adapters, including Bash user setup, permissions, Docker deployment, monitoring RAM, VRAM, disk usage, resource optimization, and team development environment maintenance.

• DPO Infrastructure Implementation: Studied Direct Preference Optimization (DPO) and presented findings. Built a DPO system for the game, collecting user feedback ("likes" on chat messages) and preparing it for future Fine-Tuning, using a React interface connected to FastAPI for seamless integration into the inference system.

• Research: Study on DPO Reading the paper Direct Preference Optimization: Your Language Model is Secretly a Reward Model, preparing a summary presentation of the findings, and presenting it to the team and audience.

• Task: Building infrastructure for DPO implementation in NanoVerse Developing a foundation for applying DPO principles in the NanoVerse game, including a mechanism where users can like messages to provide real feedback for model improvement, and preparing the infrastructure to integrate the model with the in-game chat system.