What do you mean by fine tuning a LLM ?

Large Language Models are sophisticated models trained on vast amounts of text data and are capable of understanding and generating human-like text. Fine-tuning a LLM allows you to use the pre-trained knowledge of the model to perform specific tasks such as text generation, text classification, sentiment analysis, question answering, and many more depending upon your use case.

Fine-tuning enables these models to achieve great results on your specific task with relatively little training data and computation compared to training from scratch.

Here are the steps that are performed to fine tune your Large Language Model:

Select a Pre-trained Language Model

Start with a pre-trained large language model, such as GPT (Generative Pre-trained Transformer) or BERT (Bidirectional Encoder Representations from Transformers), that has been trained on a large corpus of text data.

Task-Specific Adaptation

Modify the pre-trained language model for a specific downstream task. This might involve adding task-specific layers or adjusting the architecture as per the required task. For example, if the task is text classification, you might add a modified classification layer on top of the pre-trained model.

How do you do that?

• Understanding the Downstream Task: Before modifying your pre-trained language model, it's important to have a clear understanding of the task that is supposed to be performed. This could be any task such as text classification, sentiment analysis, language translation, or text generation.

  • Example: Suppose you have a pretrained model designed for image classification tasks, such as a convolutional neural network (CNN) trained on ImageNet. Now, imagine using this pretrained CNN for sentiment analysis on text data. In this scenario, fine-tuning the pretrained CNN for sentiment analysis on text is not going to be effective.

• Assessing Model Compatibility: Determine whether the pre-trained language model is compatible with the downstream task. Consider factors such as:

  • Model's architecture

  • Nature of the task (e.g., classification, generation)

  • Type of data involved

• Analyzing the Model Architecture: Study the architecture of the pre-trained language model to identify its components and how they process input data. This includes understanding the layers, attention mechanisms, and other components that contribute to the model. It's important to know your pre-trained model's architecture as you will be modifying this architecture in the further steps.

• Identifying Task-Specific Requirements: Analyze the requirements of the downstream task and how they differ from the pre-training objectives of the language model. For example, if the task involves text classification, you may need to add a classification layer (based on the number of outputs in your scenario) on top of the pre-trained model to make predictions.

• Modifying the Model Architecture: Based on the analysis of the downstream task and the pre-trained model, make necessary modifications to the model's architecture. This might involve adding or removing layers, freezing layers, adjusting the size of certain layers, or incorporating task-specific components such as attention mechanisms or output layers.

Training on Domain-Specific Data:

• Pick up a dataset relevant to your specific task and fine tune the language model.

• The dataset that you’ll be using will be typically smaller and definitely more that the original training data.

• The modifications you have made in the architecture will help in fine tuning the model.

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Ultimately, fine-tuning a Language Model empowers us to adapt and optimize an LLMs performance and modify it as per our use case.