LLM 0 to 1

Project Overview

An exploration of the complete process of building a large language model from scratch. The project dug into the underlying principles and implementation techniques of LLMs, including tokenizer training, data processing, and model architecture design.

Technical Architecture

• Core Language: Python
• Data Processing: Python
• Model Training: Python

Key Features

1. Tokenizer Training

The project completed tokenizer training:

• Supports multiple tokenization algorithms (BPE, Unigram, WordLevel, WordPiece)
• Built the vocabulary source library
• Optimized the tokenizer logic

2. Data Processing

The project established a complete data processing pipeline:

• Data collection and cleaning
• Data format standardization
• Train/test set splitting

3. Model Architecture Exploration

The project studied multiple model architectures in depth:

• Transformer architecture
• Pretraining code implementation
• Model parameter optimization

4. Training Pipeline

The project designed a complete model training pipeline:

• The full workflow from data preparation to model training
• Model evaluation and tuning
• Training process monitoring

Project Progress

Q4 (October–December)

• Kicked off project research
• Completed tokenizer training
• Implemented the data processing pipeline
• Explored model architecture design
• Began model training experiments

Usage

Tokenizer Configuration

# 分词器配置示例
class TokenizerConfig:
    def __init__(self):
        self.algorithms = {
            "bpe": BPE(),
            "unigram": Unigram(),
            "word_level": WordLevel(),
            "word_piece": WordPiece()
        }
    
    def train(self, data_path: str, algorithm: str = "bpe"):
        """训练分词器"""
        pass

Data Processing Pipeline

# 数据处理流程示例
class DataProcessor:
    def __init__(self):
        self.steps = [
            "data_collection",
            "data_cleaning",
            "format_conversion",
            "train_test_split"
        ]
    
    def process(self, raw_data_path: str, output_path: str):
        """处理原始数据"""
        pass

Model Architecture

# 模型架构示例
class TransformerModel:
    def __init__(self, config):
        self.embedding = EmbeddingLayer(config)
        self.encoder = EncoderStack(config)
        self.decoder = DecoderStack(config)
        self.output = OutputLayer(config)
    
    def forward(self, input_ids):
        """前向传播"""
        pass

Core Modules

1. tokenizer: The tokenizer module
2. data_processor: The data processing module
3. model_architecture: The model architecture module
4. trainer: The trainer module