Post-training GPT Neo 1B with Rust & PyTorch Pt. 1 | ML Engineering
Hi All Today we have an example of post-training a small model (GPT Neo 1B) in pure Rust. This example focuses of the training loop and model updates, assuming you've already loaded the model weights. Key Areas : 1. Model Loading : We'll use the rust-bert crate for model loading. (supports GPT-Neo) 2. Training Loop : Pure Rust implementation with tch-rs (Torch bindings) for tensor operations. 3. Optimizer : AdamW optimizer for training. View full code example below: The following is a block-by-block breakdown of the Rust post-training code above. 1. Dependencies and Imports use tch::{nn, Device, Tensor, Kind}; // Torch bindings for Rust use rust_bert::pipelines::common::ModelType; // Model types (GPT-Neo/Llama) use rust_bert::pipelines::text_generation::TextGenerationModel; // Model pipeline use anyhow::Result; // Error handling * tch - Rust binding for PyTorch * ...