Fine-tuning LLaMA 2 with Amazon SageMaker JumpStart
Lower Fine-Tuning Costs with AWS Trainium and Inferentia
Unlock the power of AWS Trainium and Inferentia-based instances through SageMaker to dramatically reduce fine-tuning costs. These specialized instances offer unparalleled performance and cost savings, making them an ideal choice for large-scale natural language processing (NLP) tasks.
Fine-Tuning Metas Code LLaMA 2 Models with SageMaker JumpStart
In this comprehensive guide, we will delve into the process of fine-tuning Metas Code LLaMA 2 models using SageMaker JumpStart. SageMaker JumpStart provides pre-built Amazon SageMaker notebooks and scripts that simplify the fine-tuning process, allowing you to focus on your specific NLP tasks.
Types of Fine-Tuning
We offer two types of fine-tuning: instruction fine-tuning and domain adaption fine-tuning. Instruction fine-tuning involves providing the model with explicit instructions on how to perform a specific task, while domain adaption fine-tuning adapts the model to a specific domain or dataset.
In this SageMaker example, we will demonstrate how to fine-tune LLaMA 2 using instruction fine-tuning. We will guide you through the entire process, from dataset preparation to model deployment.
For a complete guide on fine-tuning LLaMA 2 7-70B on Amazon SageMaker, including setup, QLoRA fine-tuning, and deployment, please refer to [this resource](link to resource).
The LLaMA 2 release introduces a wide range of pretrained and fine-tuned LLMs, spanning various scales. These models offer exceptional performance in a variety of NLP applications, including language modeling, question answering, and text generation. By leveraging Amazon SageMaker JumpStart, you can harness the power of these models and customize them for your specific needs, accelerating your NLP journey.
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