Fine-tune Code Llama on Amazon SageMaker JumpStart

This article discusses how to fine-tune Code Llama models from Meta using Amazon SageMaker JumpStart. It covers the following key points:

• What is Code Llama and why fine-tuning it is beneficial
• Step-by-step instructions for fine-tuning Code Llama models via the SageMaker Studio UI or the SageMaker Python SDK
• Fine-tuning techniques like Low-Rank Adaptation (LoRA), Int8 quantization, and Fully Sharded Data Parallel (FSDP)
• Supported hyperparameters and instance types for training
• Qualitative and quantitative evaluation of fine-tuned models using HumanEval
• Significant improvements in the performance of fine-tuned models over non-fine-tuned ones