LLM Training Toolkit: A Practical Guide to Large Language Model Training and Fine-Tuning from Scratch

An open-source project for learners, providing practical guides and experimental environments for training and fine-tuning large language models across different architectures.

• Original Author/Maintainer: mazextest2026
• Source Platform: GitHub
• Original Title: llm-training-toolkit-learning
• Original Link: https://github.com/mazextest2026/llm-training-toolkit-learning
• Source Publication/Update Time: 2026-05-31T01:47:50Z

With the boom of large language models like ChatGPT and Claude, more and more developers and researchers want to deeply understand the training principles behind these models. However, LLM training involves large-scale distributed computing, complex optimization algorithms, and massive data processing, making the entry barrier extremely high.

The LLM Training Toolkit Learning project was born as an open-source project designed specifically for learners, aiming to help users understand the training and fine-tuning process of large language models through practical methods. The project provides clear code structure, detailed comment explanations, and runnable examples, allowing beginners to gradually master the core technologies of LLM training.

This project covers the complete workflow of LLM training, including the following modules:

Data is the foundation of LLM training. The project details:

• Text data cleaning and formatting
• Selection and use of Tokenizers
• Dataset construction and loading
• Application of data augmentation techniques

The project supports experiments with multiple mainstream architectures:

• Basic Transformer Architecture: Self-attention mechanism, positional encoding, layer normalization
• GPT-style Models: Decoder-only architecture, suitable for generation tasks
• BERT-style Models: Encoder architecture, suitable for understanding tasks
• Hybrid Architecture: Experimental design combining advantages of different architectures

Provides a complete training pipeline:

• Pre-training workflow
• Supervised Fine-Tuning (SFT)
• Reward Modeling training
• Reinforcement Learning from Human Feedback (RLHF) optimization

For large-scale training needs, the project implements:

• Data Parallelism
• Model Parallelism
• Pipeline Parallelism
• ZeRO optimizer state sharding