A beginner's guide to LLM fine-tuning

Nov 8, 2023 ·

The article from Modal.com provides an overview of language model (LM) fine-tuning techniques, which are used to customize the performance of language models to specific tasks or domains. The article begins by providing an overview of language models and their basic components, including word embeddings, contextualizers, and tokenizers. It then goes on to discuss how LM fine-tuning can be used to customize the performance of language models to specific tasks and domains.

In particular, the article describes two types of fine-tuning, pre-training and post-training. Pre-training involves using the language model to generate synthetic data that is used to train a task-specific model. Post-training involves using the language model to modify the weights of a task-specific model. The article also explains how these approaches can be used together for more effective results.

Finally, the article explores several strategies for LM fine-tuning, such as freezing layers, adding layers, initializing with pre-trained weights, and using gradient clipping. These strategies are demonstrated through examples from tasks such as sentiment analysis and text classification. The article concludes by discussing some best practices and considerations for using LM fine-tuning in production applications.

Overall, this article provides a comprehensive overview of LM fine-tuning techniques, which can help to improve the accuracy of language models for specific tasks and domains. By understanding the fundamentals of LM fine-tuning and applying the described strategies, developers can create more accurate and useful language models.