Unity ML-Agents | Pretrain an LLM from Scratch with Sentence Transformers | Part 15c
0*Welcome back to our Tau LLM series! ๐*
In this episode, we're taking our project to the next level with some exciting new developments. Our highlights include:
**Data File De-duplication**: We've automated the de-duplication process for any data file loaded into our database, ensuring cleaner and more efficient training data.
**Ophrase Python Module**: We've successfully completed our ophrase module, which generates multiple paraphrases from a given sentence using Ollama, enhancing our dataset diversity.
**New Python Module for Responses**: Today, we'll implement a new module that generates responses or answers to our paraphrased sentences. This will expand our dataset from 1,000 to 9,000 records, aiming to reduce entropy and loss.
**Encoder Deduplication**: We'll also introduce a deduplication process for our encoder. This will check if an embedding already exists before generating or adding it to the database, preventing duplicate entries and keeping our index count efficient.
**Upcoming Training**: If all goes well, we'll generate new embeddings for our expanded dataset and hopefully begin training later today or tomorrow.
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