Unity ML-Agents | Pretrain an LLM from Scratch with Sentence Transformers | Part 13
0*Welcome back to our Tau LLM series! 🌟*
In this episode, we're making significant strides towards optimizing our training process and enhancing our model's performance. Our highlights include:
**Transition to Production Mode**: We'll be moving our project from the Unity editor to production mode, ensuring all necessary scripts and data files are correctly copied over during the build process.
**Training Pair Implementation**: Watch as we set up training pairs of AgentTrainers and TauAgents, and introduce a `TrainingManager` to oversee the process using SemaphoreSlim tasks for efficient management.
**Network Optimization**: We've increased our network size to 1024 with 16 layers, aiming for a balanced and efficient model. We'll discuss the benefits and challenges of this setup.
**Expanded Training Set**: We'll run our model with a training set of 250 records to observe any improvements and ensure diverse data handling.
**Reward Calculation Adjustments**: Learn about our approach to refining reward calculation by starting with simpler tasks and gradually increasing difficulty, ensuring balanced training across all columns.
**Curriculum Training**: Introducing our new `Curriculum` class to manage the incremental learning process, making our training more structured and effective.
Join us as we build, debug, and optimize our LLM project step by step. Whether you're a beginner or an experienced developer, this episode offers valuable insights into developing, testing, and enhancing an LLM using custom tools and techniques.
Stay tuned and let's get started! 🚀