I did enroll in many LLM courses for confirm this. I see that I needed to understand step by step more than show all the step, below is what summary on each step.
Step 1: Set Up Your Environment.
Choose your hardware
Install dependencies
Set up GPU acceleration (CUDA) (optional). If you use Mac M series or Arm based this may be not possible.
Step 2: Choose Your Model.
There were a lot of Pretrained model that you can choose, choose both model and parameters size (B).
eg. LLaMA (Meta), Mistral / Mixtral, Falcon, Gemma (Google), Phi (Microsoft).
Step 3: Test the Model (Inference Only).
Step 4: Prepare Your Dataset (for training).
Step 5: Choose Training Method
eg. Full fine-tuning, quantized, Parameter-Efficient Fine-Tuning (PEFT).
Step 6: Fine-Tuning the Model
Step 7: Train (by the dataset in step 4).
Step 8: Save + Use Your Fine-Tuned Model
And then back to Step 3: Test the Model (Inference Only).
You will continue to do prepare new Data set (step 4) and continue to step 8 and back to step 3 again until the result suit you.