Unity ML-Agents | Pretrain an LLM from Scratch with Sentence Transformers | Part 7
0Welcome back to our Tau LLM series! 🌟
In this episode, we dive into some exciting new developments and continue to refine our project. Our highlights include:
**Testing the `database match` Command**: We put our new `database match` command to the test. This command uses a burst job to perform a cosine similarity search across our vocabulary table in our vector database, ensuring we can efficiently find the closest matching embeddings.
**Coding the TauAgent Class**: Watch as we code our new `TauAgent` class. This class will be crucial for our model, handling the prediction of token output embeddings from input phrase embeddings.
**Implementing Reward Signals**: Learn about our approach to implementing reward signals for the `TauAgent`. We'll discuss how these signals help in training the model to improve its predictions.
**Debugging and Optimization**: Follow along as we debug and optimize our new features, ensuring everything runs smoothly and efficiently.
**Future Plans**: Once these features are in place, we'll move on to further refining our model and preparing for more advanced training and evaluation phases.
Join us on this journey as we enhance our LLM project step by step. Whether you're a beginner or an experienced developer, this episode offers valuable insights into testing, coding, and optimizing an LLM using custom tools and techniques.
Stay tuned and let's get started! 🚀