Master LLM Fine-Tuning with Hugging Face & Google Colab | Hands-on Guide...

Master LLM Fine-Tuning with Hugging Face & Google Colab | Hands-on Guide + Setup | Part3

🔥 Welcome to Part 3 of our LLM Fine-Tuning series! In this hands-on session, we move beyond theory and get into practical implementation using Hugging Face & Google Colab.

📌 In this video, you will learn:

✅ How to set up a Hugging Face account and generate an access token

✅ Setting up Google Colab with GPU/TPU and installing required libraries

✅ Selecting the right models from Hugging Face Model Hub

✅ Choosing datasets aligned with your NLP task

✅ Install Python libraries like transformers, peft, datasets, trl, accelerate , bitsandbytes, datasets

🧠bitsandbytes - lets us train models in a memory-efficient way by having quantized model in our environment.

🧠peft         - helps add adapters (extra parts) to models for fine-tuning.

🧠transformers - helps to work with large language models.

🧠datasets     - lets us load and prepare text data.

🧠trl          - gives us a trainer designed for fine-tuning with text.

🧠tokenizer    - tokenize data which can be used to fine tune model 

🧠accelerate   - helps to abstracts exactly and only the boilerplate code related to multi-GPUs/TPU/fp16 and leaves the rest of your code unchanged.

🔧 Whether you’re a beginner or an ML enthusiast, this video will show you how to bring your LLM fine-tuning ideas to life using accessible tools!

https://www.youtube.com/watch?v=6DXKKk4Ohq8