Now, if you don’t know what any of those words mean, don’t worry I’m here to help.
First off, let’s start with the basics. What is Vicuna? Well, it’s a fancy name for a large language model that can understand and generate text like a human (kinda). It was created by some smart over at Hugging Face, who are basically AI wizards.
Now, why would you want to train your own version of Vicuna using FSDP and Torchrun? Well, for starters, it’s fun! But more importantly, it can help improve the accuracy and performance of your model by distributing the workload across multiple GPUs (graphics processing units).
So, how do you go about doing this? First, you need to have a decent-sized dataset. This could be anything from news articles to social media posts as long as there’s lots of text for your model to learn from. Next, you’ll want to download the Vicuna codebase and install it on your machine (assuming you already have Python and some other necessary tools).
Once that’s done, you can start training! Here are the basic steps:
1. Prepare your data this involves cleaning it up, removing any unnecessary characters or punctuation, and converting everything to lowercase.
2. Load your dataset into memory using PyTorch Datasets (or some other similar tool).
3. Define your model architecture in a separate file (this is where you’ll specify things like the number of layers, hidden states, etc.).
4. Create a training loop that will iterate over your data and update the weights of your model using backpropagation.
5. Use FSDP to distribute the workload across multiple GPUs this involves creating a distributed dataset and wrapping your model in a DataParallel object (which is provided by PyTorch).
6. Run Torchrun on your training loop to start multi-GPU training! This will automatically split up the data into smaller chunks that can be processed simultaneously by each GPU.
7. Monitor your progress using tools like TensorBoard or Weights & Biases this will allow you to visualize things like loss, accuracy, and other important metrics over time.
8. Once your model has finished training (or when you’re satisfied with its performance), save it out as a checkpoint so that you can continue from where you left off in future sessions.
That’s the basic process for training Vicuna using FSDP and Torchrun for multi-GPU training. Of course, this is just a high-level overview if you want to dive deeper into the details, I recommend checking out some of the resources provided by Hugging Face or PyTorch (or both!).
But hey, that’s enough talk for now. Let’s get back to our training and see what kind of magic we can create with this fancy new model!