PyTorch: Training TinyLlama 1.1B in Google Colab | ML Engineering
Hi All Today we'll be loading a pre-trained model and train it on Colab. Here's a minimal example using Huggingface for transformers and accelerate for memory efficiency. View source below. Let's break down the above code. Block by block. I'm assuming you're using a notebook for this example. However in the full example in my GitHub I've included the Ops files too (Docker and k8s for GKE or AKS). I don't go into those here, I'll have a series of articles tacking MLOps in depth, this is about fine-tuning a pre-trained small LLM in Colab. Prep: Setup Environment !pip install -q transformers accelerate datasets peft bitsandbytes * transformers provides pre-trained models and training utilities * peft enables LoRA (low-rank Adaptation), reducing memory usage * bitsandbytes allows 8-bit precision to shrink model size * accelerate helps manage GPU memory and multi-GPU setups ...