Building LLMs from Scratch in Rust

Introduction

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

Key Concepts

• Transformer Blocks: Self-attention, feed-forward layers.
• Ndarray: Rust's N-dimensional array library for linear algebra.
• No Dependencies: Pure implementation for portability.

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.

Example

Train on tiny Shakespeare: Model learns basic patterns in ~100 epochs.

Evaluation

• Metrics: Perplexity on validation set.
• Trade-offs: Rust's safety vs. development speed.

Conclusion

Pure Rust LLMs enable embedded/edge AI; extend with crates like tch-rs for acceleration.