Unity ML-Agents | Pretrain an LLM from Scratch with Sentence Transformers | Part 17
0*Welcome back to our Tau LLM series! ๐*
In this episode, we're thrilled to dive into the latest developments in our project. Our highlights include:
**Ophrase Python Package**: In the last episode, we 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 integrated the new ophrase package into our terminal command kernel and added parallel processing support, significantly improving our workflow efficiency.
**Test Data Success**: We generated a test dataset using our new setup, producing about 2500 valid phrases from a total of 336k created, averaging ~18 phrases per minute.
**Oproof Python Package**: We started work on the oproof Python package, successfully renaming and updating the classes to reflect the new package and features.
In this episode, we will:
**Feature Testing**: Test the new features of the oproof package to ensure they work as expected.
**Terminal Command Implementation**: Implement the new oproof Python package into our terminal command kernel for usage with the command 'data oproof filename'.
**Continued Development**: Continue building, debugging, and optimizing our LLM project step by step.
Join us as we test and implement these exciting new features. 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