Fine-Tuning a FM
Explore the process of fine-tuning pretrained foundational models via Amazon Bedrock to specialize AI tasks. Learn methods like instruction tuning, domain adaptation, and transfer learning. Understand how to prepare datasets effectively, ensure data quality and compliance, and apply iterative updates to improve model accuracy and relevance over time.
We'll cover the following...
Fine-tuning refers to the process of taking a pretrained foundational model and adjusting its weights and parameters to perform better on a specific task. Instead of the resource-intensive process of training a model from scratch, fine-tuning allows us to leverage the knowledge embedded in a large, pretrained model and adapt it to our own data and requirements.
Amazon Bedrock provides us with access to foundational models from various providers. However, only the following FMs support fine-tuning:
Amazon Titan Text G1: Express, G1 Lite, and Premier
Amazon Titan Multimodal Embeddings G1
Amazon Titan Image Generator G1: V1 and V2
Anthropic Claude 3: Haiku
Cohere: Command and Light
Meta Llama 2: 13B and 70B
Meta Llama 3.1: 8B Instruct and 70B Instruct ...