Lab 2 - What training parameters were used to full train the LoRA tuned model?

Course Q&A Generative AI with Large Language Models
1

I am trying to replicate the results of the pre-trained LoRA optimized model in lab 2 (in my own jupyter environment), but I am not having any success. So far I have tried

  1. setting the training set to the full training set instead of every 100th sample
  2. setting max_steps to 100, max epochs to 5

I am next going to try setting the learning rate to 1e-5 instead

Does anyone know what the actual training parameters were to make this model?

2

You mean fine tuning the model before PEFT? We as mentors have only the info that you also have in the class, I would suggest to check section (for the parameters given):

2.2 - Fine-Tune the Model with the Preprocessed Dataset

But it also mentions that to fully fine tune the model it will take a few hours so its giving you the final result of the full fine tuning ready to use below!

3

no, I mean fine-tuning the model using PEFT. I know it’s going to take longer, but I wanted practice actually knowing what parameters to use to train a useful model, not just download a pre-trained model.

I ended up doing the following with good results:

  • switch to a computer with GPU and modify code to use gpu (involved putting .to('cuda') at the end of the model definitions and the tokenizer definitions
  • learning rate of 1e-4
  • no max steps
  • 5 epochs
  • full training dataset instead of only 1%
  • logging_steps=100 to reduce console output

This ended up doing 7790 training steps and took an hour and a half using a GPU. Used a g2-standard-8 vm, nvidia_L4 GPU, and 200GB SSD on google cloud.



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BASELINE HUMAN SUMMARY:
#Person1# teaches #Person2# how to upgrade software and hardware in #Person2#'s system.
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ORIGINAL MODEL:
#Person2# is considering upgrading #Person1#'s system to make up their own flyers and banners. #Person1# suggests adding a painting program to the software and upgrading the hardware. #Person2# suggests adding a CD-ROM drive.
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PEFT MODEL: #Person2# wants to upgrade #Person2#'s system and hardware. #Person1# recommends adding a painting program to #Person2#'s software and adding a CD-ROM drive.
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