How to Train your LLM: Pipeline (3/10)

The pipeline plays a fundamental role in the workflow of LLM development and deployment. In this section, we delve into the concept of the LLM pipeline and its significance in the end-to-end process. From data collection and preprocessing to model training and evaluation, each step in the pipeline contributes to the overall performance and effectiveness of the language model. We explore the key components and stages of the LLM pipeline, highlighting the importance of a well-designed and optimized pipeline for building robust and reliable language models.

How to define the right training pipeline?

• The stack on HuggingFace

  • Large corpus of permissively licensed code
  • De-duped dataset available in parquet format

• Processing in Databricks

  • Run all of our processing and transformations in Databricks
  • Allows increased control compared to Transformers library
  • Makes it easier to introduce addictional data sources

• Including propiertary data not on HF

• Preprocessing and transformations run in distributed fashion

• Tractable and extensible process

  • Lots of work done in notebooks