Unity ML-Agents | Pretrain an LLM from Scratch with Sentence Transformers | Part 13

*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! 🚀

[@darkmatter9583]

So cursor or copilot?

[@p3nGu1nZz]

copilot, but not GitHub copilot, visual code copilot, or edge copilot. windows 11 copilot in creative mode specifically. Cursor is a dope product though, still waiting for their visual studio plugin.