Custom GPT: Step-by-step guide to fine tuning Open Source LLMs
Many of you have been asking about fine tuning Open Source LLMs for a custom dataset for a specified task
Large Language Models (LLMs) are deep learning models and utilize the same techniques for fine tuning any other deep learning architecture
In this tutorial, we shall learn a Step-by-Step guide to easily Fine Tune Open Source LLMs on a custom dataset for various tasks such as text classification, question answering, Named Entity Recognition, etc
We shall build a Custom GPT and train it for text classification on emotion dataset
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Additional References
dataset: https://huggingface.co/datasets/dair-...
Various Fine tuning techniques for LLM (Peft, LoRA, QLoRA): • Fine Tuning LLMs using PeFT with limi...
Fine tuning Llama2 using Auto LLM: • Fine tuning Llama2 using AutoTrain Hu...
Download the notebook here: https://topmate.io/datahat/688726
[Use code "ytdatahat" to grab your copy for FREE]
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Additionally, here are few more useful resources for beginners and curious data enthusiasts
1. kaggle: a platform to learn, practice, compete and win cash prizes [https://www.kaggle.com/]
2. paperswithcode: website presenting the latest in machine learning and data science research and the code implementations [https://paperswithcode.com/]
3. google colab: a platform to run code, build machine learning solutions, explore the capabilities of GPU and TPU without any hassle in setting up the environment: [https://colab.research.google.com/]
Remember, "the greatest investment ever made is investment in self growth and learning"