Skip to content
Advanced Academy Reader
Exit Reader

Building LLMs from Scratch in Rust

RustGPT demonstrates creating transformers solely with Rust's ndarray for tensors, avoiding PyTorch/TensorFlow for performance and control.

advanced•3 / 6

Implementation Steps

  1. Setup Project:
    [dependencies]
ndarray = "0.15"
  2. Define Transformer Layer:
    use ndarray::Array2;
fn self_attention(q: &Array2<f32>, k: &Array2<f32>, v: &Array2<f32>) -> Array2<f32> {
    // Compute attention scores
    let scores = q.dot(&k.t()); // Simplified
    // Softmax and apply to V
    scores.dot(&v)
}
  3. Build Full Model:
    • Stack layers, add positional encoding.
  4. Training Loop:
    // Forward pass, loss computation with basic optimizer
  5. Inference:
    • Generate tokens autoregressively.
Section 3 of 6
Next →