1. Key Elements of Training a Foundation Model:
- Pre-training: The initial phase of training that requires large compute resources (GPUs, terabytes of data) and time. The model learns basic language capabilities using unstructured data through self-supervised learning.
- Fine-tuning: This is a supervised learning process that uses labeled data to update the model’s weights, improving performance on specific tasks. It adapts a foundation model to your domain-specific tasks and datasets.
- Continuous Pre-training: This process allows the model to keep learning over time using new data to stay updated.
2. Difference Between Pre-training and Fine-tuning:
- Pre-training: Uses massive, unstructured datasets to teach general language understanding.
- Fine-tuning: Uses labeled examples to improve performance on specific tasks by adjusting model weights.
3. Challenges with Fine-tuning:
- Catastrophic Forgetting: When fine-tuning on a single task, the model might lose generalization ability for other tasks. This happens because the model’s weights are updated, improving the specific task but degrading performance on other tasks.
- Full Fine-tuning: Updates all parameters of the model, but this can increase compute costs and GPU memory usage.
- Parameter-efficient Fine-tuning (PEFT): This method freezes most of the original model parameters, fine-tuning only a small number of task-specific layers, reducing memory and compute costs.
4. PEFT Techniques:
- Low-Rank Adaptation (LoRA): A PEFT technique that freezes the original weights and adds low-rank matrices to each transformer layer to adapt the model for specific tasks.
- Representation Fine-tuning (ReFT): Fine-tunes the model’s hidden representations (semantic data), without modifying the base model.
5. Multitask Fine-tuning:
- Multitask Fine-tuning uses multiple task examples in the training dataset to adapt the model to perform different tasks simultaneously.
- This method reduces catastrophic forgetting by training the model on diverse tasks, ensuring it maintains generalization ability.
6. Domain Adaptation Fine-tuning:
- Domain Adaptation Fine-tuning adapts a pre-trained model to domain-specific language or data, such as technical terms or industry jargon, using limited domain-specific data.
- Amazon SageMaker JumpStart allows you to fine-tune models with domain-specific datasets, improving performance in niche areas.
7. Reinforcement Learning from Human Feedback (RLHF):
- RLHF is a fine-tuning approach using reinforcement learning and human feedback to make models generate more human-like responses, aligning the model better with human preferences.
