This post showcases fine-tuning a large language model (LLM) using Parameter-Efficient Fine-Tuning (PEFT) and deploying the fine-tuned model on AWS Inferentia2. It discusses using the AWS Neuron SDK to access the device and deploying the model with DJLServing. It also details the necessary steps, including prerequisites, a walkthrough for fine-tuning the LLM, and hosting it on an Inf2 using SageMaker LMI Container, demonstrating how to test the model endpoint.
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Solution Overview
Efficient Fine-tuning Llama2 using QLoRa
The Llama 2 family of large language models (LLMs) with 7 billion to 70 billion parameters can be fine-tuned using the Parameter-Efficient Fine-Tuning (PEFT) approach to achieve better performance for downstream tasks.
Deploy a Fine-tuned Model on Inf2 using Amazon SageMaker
Deploy the fine-tuned model on Amazon SageMaker using AWS Inferentia2, and the AWS Neuron software development kit for high-performance and cost-effective inference workloads.
Prerequisites
Amazon SageMaker, Amazon SageMaker Domain, and Amazon SageMaker Python SDK are required for deploying the model described in this blog post.
Walkthrough
Fine-tune a Llama2-7b model using QLoRA and deploy the model into an Inferentia2 using DJL serving container hosted in Amazon SageMaker. Complete code samples and instructions can be found in this GitHub repository.
Part 1: Fine-tune a Llama2-7b model using PEFT
Quantize the base model, load the training dataset, attach an adapter layer, train a model, and merge model weight. Upload model weight to Amazon S3 for inference hosting.
Part 2: Host QLoRA model for inference with AWS Inf2 using SageMaker LMI Container
Prepare model artifacts and create an Amazon SageMaker model endpoint. Test the model endpoint and clean up resources when not required.
Conclusion
Fine-tune the Llama2-7b model with LoRA adaptor using PEFT and deploy the model to an Inf2 instance hosted in Amazon SageMaker using a DJL serving container. Validate the Amazon SageMaker model endpoint with a text generation prediction using the SageMaker Python SDK.
About the Authors
Wei Teh is a Senior AI/ML Specialist Solutions Architect at AWS, passionate about helping customers advance their AWS journey. Qingwei Li is a Machine Learning Specialist at Amazon Web Services, helping customers build machine learning solutions on AWS.
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