Fine-tuning large language models

Key Learning outcome includes:

Fine-Tuning: Adjusting a pre-trained machine learning model’s parameters on a new, specific dataset to enhance performance for a particular task.

Fine-Tuning after Pre-Training: Utilizing a model pre-trained on a broad dataset and then fine-tuning it on a specialized dataset to adapt it to specific tasks or domains.

Low-Rank Adaptation (LoRA): A parameter-efficient technique that inserts trainable low-rank matrices into a model’s layers, enabling effective fine-tuning with significantly fewer parameters.

Quantization: Reducing the precision of a model’s weights and activations (e.g., from 32-bit to 8-bit) to decrease memory usage and computational requirements, often with minimal impact on performance.

Data Pre-Processing with Prompt Template: Structuring input data into a specific format or template to guide the model’s responses during training or inference, enhancing its ability to understand and generate relevant outputs.

Data Preparation: Collecting, cleaning, and organizing data into a suitable format for training a machine learning model, ensuring quality and relevance to the target task.

Training Process: The iterative procedure where a model learns from data by adjusting its parameters to minimize a defined loss function, thereby improving its performance on the given task.

Concept of DPO: Direct Preference Optimization (DPO) is a method that directly optimizes a model based on user preferences or feedback, aiming to align the model’s outputs with desired outcomes.

DPO Data Preparation: Involves gathering and organizing user preference data to train a model using Direct Preference Optimization, ensuring the data accurately reflects user choices and priorities.

DPO Training: The process of training a model using Direct Preference Optimization, where the model learns to produce outputs that align with user preferences by directly optimizing based on feedback.

DPO Inference: Applying a model trained with Direct Preference Optimization to generate outputs that reflect learned user preferences during real-world application or deployment.