SLM FROM SCRATCH

I've been exploring how large language models are built, and it's a lot simpler to grasp if you break down the main ideas. Here's a more straightforward look at how it all works, including the specific technical details you asked for.

The Model's Parameters and Architecture

The model's parameters are all the numerical values—the weights and biases—that are learned during the training process. These parameters essentially define the model's knowledge and capabilities. For the small-scale GPT model we're discussing, the parameter count is in the millions. This is significantly less than the billions or trillions of parameters found in commercial LLMs, but it's still large enough to be a powerful learning tool. The majority of these parameters are contained within the model's embedding layers and the linear transformations inside each Transformer block.

The Transformer architecture itself is a stack of identical blocks. Each block is responsible for processing a portion of the input sequence. Increasing the number of these blocks (the n_layer hyperparameter) creates a deeper model with a higher capacity to learn complex patterns, which in turn increases the total number of parameters.

Datasets and Embeddings

The model learns from a dataset, which is the raw text it processes. A common and effective dataset for learning these concepts is Andrej Karpathy's Shakespeare dataset. It's a single text file containing all of Shakespeare's works, which is an ideal size for training a small model on a GPU in a reasonable amount of time.

To process this text, the model uses embeddings. Instead of just giving each character a single number, it represents each character as a dense vector of numbers. These vectors are what the model actually manipulates. The embeddings allow the model to capture the semantic relationships between different characters or words.

There are two main types of embeddings used:

• Token Embeddings: These vectors represent the inherent meaning or identity of each character.
• Positional Embeddings: These are additional vectors that are added to the token embeddings. They provide crucial information about the position of each character within the sequence, allowing the model to understand the order of words.

Hyperparameter Tuning and Iterations

Hyperparameters are the settings you choose to configure the model before training begins. They are not learned by the model itself. The code includes several of these, such as:

• n_embd: The dimensionality of the embeddings.
• n_head: The number of attention heads in each block.
• n_layer: The number of Transformer blocks.
• learning_rate: The size of the steps the optimizer takes.
• max_iters: The total number of training steps.

You can "play around" with hyperparameter tuning by changing these values in the code. For example, by increasing max_iters, you allow the model to train for more steps, which can lead to better performance but will take longer. Similarly, increasing n_layer will create a deeper, more powerful model, but will also increase the computational cost and time. The goal of tuning is to find a set of hyperparameters that balances performance with training time and computational resources.

The Adam Optimizer

During training, the model makes predictions and then measures its error using a loss function. The optimizer is the algorithm responsible for using this error to update the model's parameters.

The Adam optimizer is a popular and efficient choice because of its two core technical concepts:

1. Momentum: It keeps a running average of past gradients (the direction of change) to help smooth out the parameter updates. This allows the optimizer to build momentum and move more directly towards the optimal solution, avoiding local minima and speeding up convergence.
2. Adaptive Learning Rate: Adam maintains a separate, individual learning rate for each parameter. It adjusts this rate based on how frequently and how large the updates have been for that specific parameter. Parameters with consistently high gradients will receive smaller updates, while those with smaller gradients will receive larger updates. This adaptive approach makes the training process more robust and efficient.

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