Adapting LLMs for HCLS
Domain specific fine-tuning
Overview
The power of LLMs comes from their capacity to learn and generalize from extensive and diverse training data. To improve the performance of a pre-trained LLM on a specific task, we can tune the model using examples of the target task in a process known as instruction fine-tuning. This process modifies the weights of the model.
What is it?
In-context learning is useful when we don’t have direct access to the model or if we are looking to perform domain specific tasks. While pre-trained language models are trained on diverse training data, they do not perform well on a domain specific task. As you will see in demo, a pre-trained demo does well to provide high level information but when asked for a domain specific information, it failed. Fine-tuning LLM on a domain specific data, modifies weights of the model. Fine-tuning is a process that involves adapting a pre-trained model to a specific task or domain by training it further on a task-specific dataset.
How it works?
Depending on the task in hand, we can choose one of the many pre-trained language model offered in SageMaker jumpstart. Before, we can fine-tune the model, we need to prepare dataset to the format our model accepts. Amazon SageMaker provides can peform managed training on the dataset and selected language model. The fine-tuned model can then be deployed as a real-time endpoint. Using the web application, we can invoke the model. For more information, please see the architecture and follow-up resources.
Architecture
We have fine-tuned Flan-t5-xl model on MedAlpaca dataset from huggingface. The dataset was pre-processed and model was trained using Amazon SageMaker. The trained model was then deployed as a real-time endpoint invoked by Streamlit application running on EC2.
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