LLM from scratch, part 28 – training a base model from scratch on an RTX 3090

Ever found yourself staring at a blank screen, feeling both excited and terrified by the prospect of creating something groundbreaking? That’s how I felt when I decided to take on the challenge of building a Large Language Model (LLM) from scratch. Fast forward to part 28 of my journey, where I’m diving into training a base model on my trusty RTX 3090. Spoiler alert: it’s been a wild ride, complete with triumphs and the occasional facepalm moment!

The Setup: Why I Chose the RTX 3090

Let’s kick things off with the hardware. I’ve been using an RTX 3090 for a while now, and if you’ve ever wondered why it’s such a popular choice for deep learning, let me tell you—it’s a beast. With 24GB of VRAM, it’s like having a Ferris wheel of computational power at your fingertips. However, that power comes at a price. I’ve had to ensure that my workspace is efficient enough to handle the workload without turning my setup into a sauna!

The first step was installing the necessary libraries. I opted for PyTorch because, in my experience, it strikes a great balance between performance and ease of use. Here’s a quick setup snippet:

pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
pip install transformers datasets

Now, wasn't that simple? But let’s not kid ourselves—the real work begins once you start fine-tuning the model.

The Architecture: Building from the Ground Up

Now, onto the fun part: defining the architecture of my base model. In my previous experiments, I’ve seen how even minor changes in design can drastically affect performance. I decided to go for a standard transformer architecture because, well, why fix what isn’t broken?

Here’s a quick snippet to give you a sense of what I put together:

import torch
import torch.nn as nn

class SimpleTransformer(nn.Module):
    def __init__(self, input_dim, num_heads, num_layers):
        super(SimpleTransformer, self).__init__()
        self.encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=input_dim, nhead=num_heads),
            num_layers=num_layers
        )

    def forward(self, x):
        return self.encoder(x)

model = SimpleTransformer(input_dim=512, num_heads=8, num_layers=6)

This little nugget of code sets the foundation for my LLM. An aha moment for me here was realizing the importance of hyperparameter tuning. Selecting the right number of layers and heads can make or break a model. I’ve spent countless hours experimenting with these values, and trust me—the effort pays off.

Training: The Good, the Bad, and the Computationally Intensive

With the model in place, it was time to feed it some data. I used a dataset that I scraped from various sources. However, I quickly learned that not all data is created equal! I had my fair share of noisy data issues, which led to less-than-stellar results. So, my advice? Clean your dataset like it’s your bathroom before guests arrive!

Here’s how I prepared my dataset using the Hugging Face datasets library:

from datasets import load_dataset

dataset = load_dataset("wikitext", "wikitext-2-raw-v1")
train_data = dataset['train']

I trained the model for a few epochs, and to my surprise, it started generating coherent text! Yet, it wasn’t perfect. There were moments where I’d read the output and think, “What on earth is this?” It was a humbling experience, showing me the limitations of training from scratch. Every output was a reflection of both my choices and the quality of the training data.

Troubleshooting: When Things Go South

So, what happens when your model isn’t learning? I ran into a few hiccups that led to endless debugging sessions. One of the major issues was gradient explosion. If you’ve ever dealt with this, you know it can make you question your life choices. Luckily, I discovered gradient clipping, which helped stabilize the training process. Here’s a simple implementation:

torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)

This little piece of code became my savior. It gave me a sense of control over my training process, reducing those moments of utter frustration when I saw loss values skyrocketing out of control.

Real-World Applications: Where This All Leads

Now that I’ve trained a basic model, you might wonder—what’s next? Well, there’s a world of possibilities. I’ve been using this model to generate code snippets for small projects, and let me tell you, it’s like having an assistant who never sleeps.

But it’s also made me think about the ethical implications of AI. The more I experiment, the more I realize how important it is to consider biases in data. What if I told you that your model can perpetuate stereotypes if fed the wrong kind of data? It’s a sobering thought and one that every developer should contemplate.

Future Thoughts: The Path Ahead

As I wrap up this chapter of my LLM journey, I can’t help but feel a mix of excitement and uncertainty. I’m genuinely excited about the future of AI and how it can transform our world, but I also feel a sense of responsibility. With great power comes great responsibility, after all.

Moving forward, I plan to explore more advanced techniques like fine-tuning on specific tasks and experimenting with mixed-precision training for efficiency. If you’re also on this path, I’d love to hear about your experiences. What tools are you using? What challenges have you faced? Let’s keep the conversation going!

Building an LLM from scratch has been one of the most rewarding experiences of my developer journey. It’s taught me to embrace failures, celebrate small victories, and continuously push my boundaries. So grab your RTX 3090, and let’s build something amazing together!