How to Build an LLM from Scratch | An Overview

Summary

The video provides an overview of key considerations when building a large language model from scratch in 2024, a now more feasible endeavor thanks to advances in AI. It steps through the process, from curating high-quality diverse training data, to designing an efficient Transformer architecture, to leveraging techniques like mixed precision to train at scale, to evaluating model performance on benchmarks. While still resource-intensive, building an LL.M may make sense for certain applications. The video concludes by noting base models are usually then customized via prompt engineering or fine-tuning.

Takeaways

• 😊 Building LLMs is gaining popularity due to increased interest after ChatGPT release
• 📈 Costs to train LLMs range from $100K (10B parameters) to $1.5M (100B parameters)
• 🗃️ High quality and diverse training data is critical for LLM performance
• ⚙️ Transformers with causal decoding are the most popular LLM architecture
• 👩‍💻 Many design choices exist when constructing LLM architectures
• 🚦 Parallelism, mixed precision, and optimizers boost LLM training efficiency
• 📊 Hyperparameters like batch size, learning rate, and dropout affect stability
• 📈 LLMs should balance model size, compute, and training data to prevent over/underfitting
• ✅ Benchmark datasets help evaluate capabilities on tasks like QA and common sense
• 🔄 Fine-tuning and prompt engineering can adapt pretrained LLMs for downstream uses

Q & A

What are the four main steps involved in building a large language model from scratch?

The four main steps are: 1) Data curation 2) Model architecture 3) Training the model at scale 4) Evaluating the model.

What type of model architecture is commonly used for large language models?

Transformers have emerged as the state-of-the-art architecture for large language models.

Why is data curation considered the most important step when building a large language model?

Data curation is critical because the quality of the model is driven by the quality of the data. Large language models require large, high-quality training data sets.

What are some key considerations when preparing the training data?

Some key data preparation steps include: quality filtering, deduplication, privacy redaction, and tokenization.

What are some common training techniques used to make it feasible to train large language models?

Popular training techniques include mixed precision training, 3D parallelism, zero redundancy optimizers, checkpointing, weight decay, and gradient clipping.

How can you evaluate a text generation model on multiple choice benchmark tasks?

You can create prompt templates with a few shot examples to guide the model to return one of the multiple choice tokens as its response.

What are some pros and cons of prompt engineering versus model fine-tuning?

Prompt engineering avoids changing the original model but requires more effort to create effective prompts. Fine-tuning adapts the model for a specific use case but risks degrading performance on other tasks.

What are some examples of quality filtering approaches for training data?

Classifier-based filtering using a text classification model, heuristic-based rules of thumb to filter text, or a combination of both approaches.

What considerations go into determining model size and training time?

You generally want around 20 tokens per model parameter in the training data. And a 10x increase in model parameters requires around a 100x increase in computational operations.

Why might building a large language model from scratch not be necessary?

Using an existing model with prompt engineering or fine-tuning is better suited for most use cases. Building from scratch has high costs and only makes sense in certain specialized cases.