Train Your Own LLM: A Complete Guide from Scratch

Data Preparation and Tokenization

• 🚀 Data extraction involves exporting chat messages from apps like WhatsApp or Telegram, followed by data cleaning to remove unwanted tags, links, and personal information.
• 💡 Byte Pair Encoding (BPE) is used for tokenization, converting text into numerical IDs, balancing vocabulary size and sequence length.
• 🔑 The process involves training a tokenizer from scratch using BPE, creating a vocabulary, and encoding the text sequence.

Transformer Architecture and Pre-training

• 🧠 The Transformer architecture, specifically the decoder part for autoregressive models, is explained, including token embedding, positional encoding, self-attention, residual connections, layer normalization, and feed-forward layers.
• ⚙️ Key hyperparameters like block size, embedding size, number of heads, and number of blocks are defined to construct the model.
• 📊 Pre-training involves splitting data into training and validation sets, feeding batches to the model, and optimizing weights to predict the next token.
• 📈 Monitoring training and validation loss is crucial to identify underfitting or overfitting.

Fine-tuning for Conversational AI

• 💬 Fine-tuning adapts a pre-trained base model for specific tasks, such as mimicking a person's communication style.
• 🎯 Two main fine-tuning methods are discussed: full fine-tuning (instruction fine-tuning) and parameter-efficient fine-tuning (PEFT), like LoRA.
• 🧩 LoRA efficiently fine-tunes models by adding and training only a small number of new parameters, creating adapters that can be attached to the base model.
• 📝 Fine-tuning datasets can be created with or without conversational context. The 'no context' approach trains on individual user-assistant message pairs, while the 'with context' approach uses longer conversational histories.
• ⚠️ Masking techniques are employed in the target data to ensure the model focuses on generating the assistant's response rather than the input prompts.

Scaling and Cloud Training

• ☁️ Training large models on extensive datasets like AtlasSet (900 million characters) requires significant computational resources, often necessitating cloud-based GPU training.
• 💾 For large datasets, memory-mapped files (e.g., using NumPy) are recommended over formats like pickle to manage memory efficiently.
• ⏳ Training large models can take days or weeks, making cloud platforms like Google Colab, Kaggle, or Lambda essential for practical development.
• 📊 Experimentation with hyperparameters, model size, and data batching strategies (e.g., overlapping vs. non-overlapping batches) is key to optimizing performance and training time.

Advanced Fine-tuning Techniques

• 🗣️ Conversational fine-tuning with masked targets teaches the model to generate responses within a dialogue context, improving coherence.
• 🎭 Masking specific parts of the target sequence (like system prompts and user turns) ensures the model learns to generate only the assistant's response.
• 📈 While smaller models may overfit on limited fine-tuning data, careful checkpoint selection and hyperparameter tuning can yield satisfactory results.
• 💡 The final goal is to create a language model that can understand and generate text in a specific language or mimic a particular communication style.