Unity ML-Agents | Pretrain an LLM from Scratch with Sentence Transformers | Part 16
0*Welcome back to our Tau LLM series! ๐*
In this episode, we're excited to share some major advancements in our project. Our highlights include:
**Ophrase Python Package**: We've successfully migrated our ophrase module into its own Python package and installed it into our ml-agents virtual environment. This package uses Ollama and Llama 3.1 on the backend to generate multiple paraphrases from a given sentence, enhancing our dataset diversity.
**Terminal Command Integration**: We've implemented the new ophrase package into our terminal command kernel and added parallel processing support, significantly improving our workflow efficiency.
**Test Data Success**: We've successfully generated a test dataset using our new setup. Now, we'll focus on generating a new training dataset with additional paraphrased data.
**Embedding Generation**: After creating the new training dataset, we'll generate embeddings and save a new database file.
**Training Runs**: We'll perform several training runs using the new training data and measure their performance with our evaluation dataset.
Join us as we continue to 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! ๐