#Finetuning

Discover an informative guide for developers interested in understanding the core elements of large language models (LLMs). This tutorial covers the creation of a GPT-like model from scratch using PyTorch, with focus on data input pipelines, main architecture components, and pretraining techniques. It also includes instructions for loading and finetuning with pretrained weights via open-source libraries such as LitGPT. A convenient cloud setup is available for practical experimentation and smooth code execution.

Discover SwissArmyTransformer, a unified codebase for integrating model-agnostic components into Transformer-based models. Utilize DeepSpeed and model parallelism for efficient pretraining and finetuning of large-scale models with ease. Implement prefix-tuning in models such as GLM and GPT to boost performance with minimal effort. Leverage extensive training support on multiple GPUs or nodes, accommodating models like T5-10B and experimental ones like CogView2. SwissArmyTransformer offers a comprehensive environment for developing and optimizing Transformer variants designed for various AI tasks.