Summary of Build a Small Language Model (SLM) From Scratch

Summary of "Build a Small Language Model (SLM) From Scratch"

This comprehensive tutorial by Dr. Raj Gandekar, PhD in machine learning from MIT and co-founder of Vijuara AI Labs, guides viewers through building a fully functional small language model (SLM) from scratch. The video covers theoretical concepts, mathematical foundations, and practical coding steps, culminating in a model capable of generating coherent English stories. The tutorial is production-level, emphasizing efficiency and real-world applicability rather than a toy example.

Key Technological Concepts and Features Covered:

  1. Small Language Models (SLMs)
    • Definition: Models with fewer than ~1 billion parameters, typically in the range of 10-50 million parameters.
    • Motivation: Large models like GPT-3 (175B parameters) and GPT-4 (~1T parameters) are computationally expensive; smaller models are faster, easier to run locally, and more practical for many applications.
    • Example: The tutorial builds a ~15 million parameter model, which is thousands of times smaller than GPT-3/4 but still capable of coherent text generation.
  2. Dataset: Tiny Stories
    • A curated dataset of 2 million short stories for 3-4 year old children, generated by GPT-4.
    • Purpose: A small, task-specific dataset that captures the nuances of English grammar and structure without requiring massive internet-scale data.
    • Split: 2 million training stories, 20,000 validation stories.
    • Advantage: Enables training an SLM effectively on a small, domain-specific corpus.
  3. Data Preprocessing and Tokenization
    • Tokenization Methods:
      • Word-based tokenization: Large vocabulary (~500,000 words), problematic for spelling mistakes and memory.
      • Character-based tokenization: Too many tokens, inefficient.
      • Subword tokenization (Byte Pair Encoding - BPE): Optimal balance by breaking rare words into subwords; vocabulary size manageable.
    • Use of GPT-2 tokenizer (BPE-based).
    • Efficient storage of tokenized data in .bin files using memory-mapped arrays to avoid RAM overload and speed up training.
    • Data batching (e.g., 1024 batches) for efficient processing.
  4. Input-Output Pair Creation
    • Language modeling framed as a next-token prediction task.
    • Inputs: Sequences of tokens of fixed context size (e.g., 4 tokens).
    • Outputs: Inputs shifted right by one token (the "next token").
    • Batching: Multiple input-output pairs processed simultaneously.
    • Training objective: Predict the next token for every position in the input sequence.
    • Importance of context size and batch size explained.
  5. Model Architecture
    • Based on Transformer architecture with three main components:
      • Input block: Token embeddings + positional embeddings.
      • Processor block: Multiple transformer blocks, each containing:
        • Layer Normalization (to stabilize training).
        • Multi-head causal self-attention (captures relationships between tokens without peeking into future tokens).
        • Feed-forward neural network with GELU activation (expands and compresses embedding dimensions).
        • Dropout layers for regularization.
        • Residual (shortcut) connections to prevent vanishing gradients.
      • Output block: Final layer normalization + output head (linear layer projecting embeddings to vocabulary size logits).
    • Multi-head attention explained as queries, keys, values with scaled dot-product attention and causal masking.
    • Model configuration example: vocab size 50,257; block size (context) 128; embedding dim 384; 6 transformer blocks; 6 attention heads per block.
    • Model parameters initialized with Gaussian distributions for stable training.
  6. Loss Function
    • Cross-entropy loss (negative log likelihood) between predicted token probabilities and true next tokens.
    • Softmax applied to logits to convert to probability distributions.
    • Loss computed over all tokens in a batch.
    • Explanation of flattening logits and targets for batch-wise loss computation.
  7. Training Pipeline
    • Use of AdamW optimizer with weight decay.
    • Learning rate scheduling: linear warm-up followed by cosine decay for stability.
    • Automatic Mixed Precision (AMP) via torch.autocast to speed up training by using float16 where safe.
    • Gradient accumulation to simulate large batch sizes on limited GPU memory (e.g., accumulate gradients over 32 steps before updating).
    • Efficient data transfer to GPU using pinned memory and non-blocking transfers.
    • Model checkpointing: saving best model parameters to avoid retraining.
    • Training run example: 20,000 iterations on A100 GPU (~30 minutes), with training and validation loss smoothly decreasing and close to each other, indicating no overfitting.
  8. Inference and Text Generation
    • Autoregressive generation:
      • Input sequence passed through model to get logits.
      • Softmax applied, next token selected (top ...

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