LLM experimentation management and tracking using HuggingFace and Weights and Bias

Fine tuning LLMs for domain specific tasks like classification (one some private dataset) is not easy. We have to understand lot of concepts and it I have struggled a lot to find a proper way to fine tune and evaluate our fine tuned models on tasks like classification.

  • How a dataset can help a model to achieve to provide refined results
  • how can we contraint our model’s output
  • how to construct dataset with external prompts so that we can cast a language completion problem to a classification like problem
  • how to fine tune a 7B model on a consumer gpu
  • common problems while loading a peft model (saved in local) during inference
  • how to build an overall analytical pipeline to asses LLM’s performance on quality, speed, and reliability
  • Other different insights and best practices.

My latest blog covers it all, and being a three part blog series, more to come. In this blog I shared all the potential common best practices for fine tuning a large language models using Hugging Face and utilize Weights and Bias to effectively manage our experimentation and track our model based on different performance parameters.

Please do check out here