Training a Large Language Model
As demand for large language models has grown substantially, so is the curiosity and demand for building and experimenting with building own model. Often training a large language model is broken into two broad stages.
- Pre-training
- Fine-tuning
In this tutorial, we'll delve into the details of each stage, exploring how they work together to create powerful and versatile language models
Pre-training
Pre-training lays the foundation for language models by exposing them to vast amounts of unlabeled text data. During this stage, models, often based on transformer architectures like GPT (Generative Pre-trained Transformer) or BERT (Bidirectional Encoder Representations from Transformers), learn to predict missing words or masked tokens within sentences.
Fine-tuning
Fine-tuning in deep learning is a form of transfer learning. The goal is to optimize the model’s performance on a new, related task without starting the training process from scratch.
While pre-training imparts general language understanding, fine-tuning refines the model for specific tasks or domains. This stage involves training the model on labeled datasets relevant to the target application. The pre-trained model serves as a powerful starting point, and the model learns to adapt to the intricacies of the target use-case. The process of fine-tuning is similar to any supervised training phase with the caveat that it has a starter model with weights available (pre-trained model).
The overall architecture of the pre-trained model remains mostly intact during the fine-tuning process. The idea is to leverage the valuable features and representations learned by the model from the vast dataset it was initially trained on and adapt them to tackle a more specific task.
Why fine-tune?
Training an LLM from scratch can be extremely time-consuming and prohibitively expensive. Fine-tuning allows us to build upon a pre-trained model, significantly reducing the time and resources required to achieve better results for specific task compared to a generalized pre-trained model. By starting with a model that has already learned many generic features helps us skip the pre-training stage and focus on adapting the model to the specific task at hand.
There are broadly two pieces of the puzzle involed in fine tuning:
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Input Training datasets: Labeled datasets for tasks such as question answering, or named entity recognition, text-to-sql are used for fine-tuning. These datasets guide the model to specialize in the nuances of the desired application.
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Fine-tuning logic to carry out actual fine-tuning task that helps adjust the pre-trained model's parameters to the needs of the task and creates a new updated model.
Steps involved in fine-tuning a model
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Create an input fine-tuning dataset
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Select a pre-trained model
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Select necessary training parameters
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Trigger the training loop
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Evaluate the results
