Building a Large Language Model from Scratch: A Developer's Deep Learning Journey

Project Source:

• Author/Maintainer: Yajas565
• Platform: GitHub
• Repository Link: https://github.com/Yajas565/llm-from-scratch-journey
• Release Date: May 28, 2026

Core Content: This project is based on Sebastian Raschka's book 'Build a Large Language Model (From Scratch)', implementing the complete process of an LLM from scratch, covering core modules like tokenizer (BPE), embedding layer, self-attention mechanism, GPT model assembly, pre-training, and fine-tuning. It aims to help developers deeply understand the internal principles of LLMs, rather than just staying at the API call level.

LLMs like GPT and Llama are powerful, but they remain a "black box" for most developers. Yajas565 started this project out of curiosity about "how large language models actually work", choosing to build from scratch to gain the deepest understanding. As Donald Knuth said: "If you really understand something, you should be able to build it from scratch."

Learning Resources: Sebastian Raschka's 'Build a Large Language Model (From Scratch)', which uses basic PyTorch tensor operations without relying on high-level frameworks, explaining the "why" and "how to implement" each component of LLMs.

Project Modules:

1. Tokenizer (BPE algorithm)
2. Embedding layer and data loader
3. Self-attention mechanism
4. GPT text generation model
5. Pre-training
6. Fine-tuning

• Tokenizer: Uses BPE algorithm to balance vocabulary size and expressiveness, implementing encoder/decoder and special token handling.
• Embedding layer: Converts tokens into vectors, adds positional encoding to capture sequence order, supports efficient batch processing and sliding window sampling.
• Self-attention: Implements scaled dot-product attention (with causal masking) and multi-head attention to capture multi-dimensional information.
• GPT model: Stacks Transformer blocks (attention + feed-forward network + layer normalization + residual connection), supports greedy decoding and temperature sampling for text generation.

• Pre-training: Self-supervised learning based on large-scale unlabeled text (predicting the next token), using cross-entropy loss, combined with learning rate warm-up, gradient accumulation, and mixed-precision training, monitoring loss and perplexity.
• Fine-tuning:
  • Instruction fine-tuning: Turning the model into an instruction follower;
  • LoRA: Low-rank adaptation technology for efficient parameter fine-tuning;
  • Classification fine-tuning: Adapting to downstream tasks like text classification.

1. Understanding attention mechanisms: Solved by manually deriving formulas, calculating small examples, and visualizing attention weights.
2. Training non-convergence: Adjust learning rate (warm-up + cosine annealing), add gradient clipping, check data preprocessing.
3. Poor generation quality: Increase data volume/training epochs, adjust generation parameters (temperature, top-k), improve model capacity.
4. Resource constraints: Use small-scale models, cloud platform GPUs, parameter-efficient fine-tuning techniques like LoRA.

Value:

• Learners: Gain a deep understanding of the underlying principles of LLMs;
• Researchers: Discover hidden details under framework abstractions;
• Engineers: Diagnose model problems faster and customize architectures.

Future Directions:

• Architecture improvements: Flash Attention, RoPE positional encoding, non-Transformer models (Mamba/RWKV);
• Training optimization: Distributed training, Lion optimizer, quantization training;
• Application expansion: Multimodal models, tool calling, dialogue systems.

Summary: As Raschka said, "The best way to understand LLMs is to build them", and this project is a practice of this idea.