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Basic concepts to know before building a Large Language Model

Updated: Jan 25

Building a large language model (LLM) involves understanding and implementing several key concepts across machine learning, natural language processing (NLP), and deep learning.



LLM concepts
LLM concepts


Here's a breakdown of the foundational concepts:


1. Data Preparation

  • Text Corpus: Collecting and cleaning massive amounts of text data (e.g., books, articles, code, dialogues).

  • Tokenization: Splitting text into smaller units (tokens), such as words, subwords, or characters.

  • Vocabulary: Defining a fixed set of tokens (word-level, byte-pair encoding (BPE), or WordPiece).

  • Preprocessing:

    • Lowercasing, removing special characters, or normalizing text.

    • Handling out-of-vocabulary (OOV) tokens.

    • Adding special tokens (e.g., [CLS], [PAD], [SEP]).

2. Model Architecture

  • Transformer Architecture: The backbone of modern LLMs.

    • Self-Attention Mechanism:

      • Computes relationships between tokens in the input sequence.

      • Scaled dot-product attention formula.

    • Multi-Head Attention: Combines multiple attention heads for richer representations.

    • Feedforward Layers: Fully connected layers applied to attention outputs.

    • Positional Encoding: Adds sequence order information to token embeddings.

  • Encoder-Decoder vs. Encoder-only vs. Decoder-only:

    • Encoder-Decoder: For tasks requiring input-to-output mappings (e.g., translation).

    • Encoder-only: For tasks like classification or retrieval (e.g., BERT).

    • Decoder-only: For tasks involving text generation (e.g., GPT).

3. Training Objectives

  • Masked Language Modeling (MLM):

    • Predicts masked tokens in a sequence (e.g., BERT).

  • Causal Language Modeling (CLM):

    • Predicts the next token in a sequence (e.g., GPT).

  • Sequence-to-Sequence (Seq2Seq):

    • Generates a target sequence from an input sequence (e.g., T5, BART).

  • Contrastive Loss:

    • For tasks like retrieval or representation learning.

4. Training Techniques

  • Optimization:

    • Gradient descent using optimizers like Adam or AdamW.

    • Learning rate schedulers (e.g., cosine annealing, warm-up).

  • Batching:

    • Training with batches for efficient computation.

    • Padding to handle sequences of varying lengths.

  • Regularization:

    • Dropout, layer normalization, weight decay.

  • Mixed-Precision Training:

    • Speeds up training while reducing memory usage.

5. Scaling Considerations

  • Model Parameters:

    • Increasing the number of layers, attention heads, and hidden dimensions.

  • Data Parallelism:

    • Splitting data across GPUs for distributed training.

  • Model Parallelism:

    • Splitting the model across GPUs for handling large parameter counts.

  • Memory Optimization:

    • Gradient checkpointing and offloading to manage memory usage.

6. Evaluation Metrics

  • Perplexity: Measures how well the model predicts a sequence.

  • BLEU/ROUGE: For tasks like translation or summarization.

  • Accuracy/F1-Score: For classification tasks.

  • Human Evaluation: For assessing text quality in generative tasks.

7. Pretraining and Fine-Tuning

  • Pretraining:

    • Training on large datasets to learn general language representations.

  • Fine-Tuning:

    • Adapting the pretrained model to specific tasks or domains.

  • Few-Shot/Zero-Shot Learning:

    • Using the model without fine-tuning or with minimal task-specific examples.

8. Hardware and Computational Resources

  • GPUs/TPUs: Essential for handling the computational demands of LLMs.

  • Distributed Training Frameworks: PyTorch Distributed, DeepSpeed, or TensorFlow.

  • Memory Management:

    • Techniques like activation checkpointing and gradient accumulation.

9. Ethical and Practical Considerations

  • Bias Mitigation:

    • Addressing biases in training data and model outputs.

  • Data Privacy:

    • Ensuring compliance with privacy laws (e.g., GDPR).

  • Resource Efficiency:

    • Reducing energy consumption and optimizing compute.

10. Fine-Tuning for Applications

  • Text Generation: Dialogue systems, creative writing.

  • Classification: Sentiment analysis, spam detection.

  • Information Retrieval: Question answering, search systems.

  • Summarization: Generating concise summaries of longer texts.

  • Translation: Converting text from one language to another.

  • Code Generation: Assisting in programming and debugging.


These foundational concepts can be extended or tailored depending on the specific goals, such as building a general-purpose LLM or a task-specific application.

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