One of the projects we are working on involves generating code for a custom dialect of a programming language using a large language model (LLM). With a dataset of instructions and their corresponding implementations, we aim to fine-tune a model to automate this process. Given the rapid advancements in AI, fine-tuning LLMs can significantly enhance their performance for specific tasks, offering tailored solutions that generic models might not provide.
In our pursuit to fine-tune this model, we turned to AWS Bedrock. AWS Bedrock offers a fully managed, user-friendly environment for training and deploying custom models, making it an attractive choice for our experimentation and development needs. Its promise of streamlined integration and robust infrastructure seemed ideal for our use case. In this blog post, we’ll delve into the journey of fine-tuning an LLM with AWS Bedrock. We’ll explore the platform’s standout features that facilitated our project and discuss the challenges we faced along the way.
Read the full blogpost on our Medium channel (code included).
