Microgpt explained interactively
14 hours ago
- #GPT
- #Machine Learning
- #Neural Networks
- Andrej Karpathy created a 200-line Python script called MicroGPT that trains and runs a GPT model from scratch with no dependencies.
- The model is trained on 32,000 human names to learn statistical patterns and generate new, plausible names.
- Text is converted into integers using a simple tokenizer where each character is assigned a unique ID, including a special BOS (Beginning of Sequence) token.
- The model's core task is predicting the next token in a sequence, sliding through each position to create training examples.
- Logits (raw scores) are converted into probabilities using the softmax function, which ensures they are positive and sum to 1.
- Cross-entropy loss measures prediction error, punishing confident wrong answers severely.
- Backpropagation is used to compute gradients for each parameter, enabling the model to improve by adjusting weights.
- Token embeddings convert token IDs into learned vectors, combining token and position embeddings for input.
- Attention mechanisms allow tokens to gather information from previous positions via queries, keys, and values.
- The model pipeline includes embedding, normalization, attention, residual connections, MLP (multilayer perceptron), and output projection.
- Training involves running forward and backward passes, using the Adam optimizer to update parameters efficiently.
- Inference generates text by sampling from the model's predicted probabilities, with temperature controlling output diversity.
- MicroGPT's core concepts are identical to larger models like ChatGPT, with differences lying in scale and engineering optimizations.