Under the hood, Microgpt, particularly Andrej Karpathy’s minimalist implementation, operates by encapsulating the fundamental algorithmic essence of a Generative Pre-trained Transformer (GPT) model. Its core function is to predict the next token in a sequence based on the preceding tokens. This process begins with a tokenizer, which converts raw input text into a sequence of numerical identifiers, or token IDs. In Microgpt’s simplified design, this is often a character-level tokenizer, where each character in the input vocabulary is assigned a unique ID. These token IDs are then transformed into dense numerical vectors called embeddings. These embeddings are crucial because neural networks process numbers, not raw text. Additionally, positional embeddings are added to these token embeddings, providing the model with information about the order and position of each token within the sequence.
Once the input sequence is represented as a series of combined token and positional embeddings, it passes through a stack of transformer blocks. Each transformer block is composed primarily of two sub-layers: a multi-head self-attention mechanism and a position-wise feed-forward network. The self-attention mechanism allows the model to weigh the importance of different tokens in the input sequence relative to each other, capturing long-range dependencies and contextual relationships. The feed-forward network then processes these context-aware representations independently for each position. After passing through these layers, the output of the final transformer block is fed into a linear layer, which projects the processed embeddings back into a vocabulary-sized space, generating logits (unnormalized scores) for each possible next token. A softmax function is then applied to these logits to convert them into probabilities, indicating the likelihood of each token being the next in the sequence. During inference, the token with the highest probability (or sampled based on temperature) is selected as the prediction, and this process repeats autoregressively to generate a complete output sequence.
The entire system, including the embedding process, transformer blocks, and output layer, is trained end-to-end. The training objective is to minimize the difference between the model’s predicted next token probabilities and the actual next token in the training data, typically using a loss function like cross-entropy. Microgpt often includes a simple autograd engine to compute gradients and update model parameters during training. While the original Microgpt does not directly interact with external vector databases, the concept of embeddings is central to its operation. In more advanced Microgpt-inspired agents, the internal embedding generation can be extended to create query embeddings for semantic search in external vector databases like Milvus . This allows the agent to retrieve relevant external context, which can then be integrated into the input sequence, effectively expanding the model’s knowledge beyond its initial training data and enhancing its ability to generate informed responses.