Understanding Large Language Models from Scratch: Experimental Implementation of Core Components

This article introduces the LLM research workspace created by Samrat Raj Sharma. By implementing core components such as tokenization, Transformer architecture, attention mechanisms, and GPT-style models from scratch, it uses the concept of "learning by building" to help developers gain an in-depth understanding of the internal working principles of modern large language models, going beyond the level of merely using pre-trained models.

Most developers currently stay at the level of using pre-trained LLMs and lack an in-depth understanding of their internal operating mechanisms. The core philosophy of this workspace is "learning by building": instead of relying on ready-made components encapsulated in advanced libraries, it involves hands-on implementation of each module to understand key processes such as Transformer layer operation, attention allocation, and token probability calculation through practice.

Covers practical explorations of language modeling (basic tasks like next-token prediction and context modeling), tokenization techniques (subword tokenization, BPE algorithm, vocabulary construction, etc.), Transformer architecture (self-attention, multi-head attention, positional encoding, residual connections, etc.), attention mechanisms (scaled dot-product, QKV representation, etc.), and GPT-style models (autoregressive generation, decoder-only architecture, etc.).

Experiments with various text generation decoding strategies: greedy decoding (selecting the token with the highest probability, deterministic but boring), temperature sampling (adjusting randomness), Top-k sampling (limiting candidate tokens), Top-p sampling (dynamic candidate set), etc. These strategies affect the diversity and quality of generated text.

This workspace provides a complete learning path from theory to practice, helping learners understand what LLMs are, why they work, and how to build them. For large model researchers or engineering developers, understanding the underlying mechanisms helps in better tool usage, problem debugging, and new architecture development—its long-term value is higher than merely calling APIs.

Cutting-edge explorations include architecture expansion (efficient attention, context window expansion), training optimization (LoRA, instruction fine-tuning), model evaluation, etc. Future plans include exploring advanced Transformer optimization, distributed training, mixture-of-experts architecture, RAG, multimodal models, etc., with the goal of bridging the gap between simplified implementations and production-level models.